import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
import itertools
application_data = pd.read_csv(r'application_data.csv')
previous_application = pd.read_csv(r'previous_application.csv')
columns_description = pd.read_csv(r'columns_description.csv',skiprows=1)
print ("application_data :",application_data.shape)
print ("previous_application :",previous_application.shape)
print ("columns_description :",columns_description.shape)
application_data : (307511, 122) previous_application : (1670214, 37) columns_description : (159, 5)
pd.set_option("display.max_rows", None, "display.max_columns", None)
display("application_data")
display(application_data.head(3))
'application_data'
| SK_ID_CURR | TARGET | NAME_CONTRACT_TYPE | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | NAME_TYPE_SUITE | NAME_INCOME_TYPE | NAME_EDUCATION_TYPE | NAME_FAMILY_STATUS | NAME_HOUSING_TYPE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | OWN_CAR_AGE | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | OCCUPATION_TYPE | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | WEEKDAY_APPR_PROCESS_START | HOUR_APPR_PROCESS_START | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | ORGANIZATION_TYPE | EXT_SOURCE_1 | EXT_SOURCE_2 | EXT_SOURCE_3 | APARTMENTS_AVG | BASEMENTAREA_AVG | YEARS_BEGINEXPLUATATION_AVG | YEARS_BUILD_AVG | COMMONAREA_AVG | ELEVATORS_AVG | ENTRANCES_AVG | FLOORSMAX_AVG | FLOORSMIN_AVG | LANDAREA_AVG | LIVINGAPARTMENTS_AVG | LIVINGAREA_AVG | NONLIVINGAPARTMENTS_AVG | NONLIVINGAREA_AVG | APARTMENTS_MODE | BASEMENTAREA_MODE | YEARS_BEGINEXPLUATATION_MODE | YEARS_BUILD_MODE | COMMONAREA_MODE | ELEVATORS_MODE | ENTRANCES_MODE | FLOORSMAX_MODE | FLOORSMIN_MODE | LANDAREA_MODE | LIVINGAPARTMENTS_MODE | LIVINGAREA_MODE | NONLIVINGAPARTMENTS_MODE | NONLIVINGAREA_MODE | APARTMENTS_MEDI | BASEMENTAREA_MEDI | YEARS_BEGINEXPLUATATION_MEDI | YEARS_BUILD_MEDI | COMMONAREA_MEDI | ELEVATORS_MEDI | ENTRANCES_MEDI | FLOORSMAX_MEDI | FLOORSMIN_MEDI | LANDAREA_MEDI | LIVINGAPARTMENTS_MEDI | LIVINGAREA_MEDI | NONLIVINGAPARTMENTS_MEDI | NONLIVINGAREA_MEDI | FONDKAPREMONT_MODE | HOUSETYPE_MODE | TOTALAREA_MODE | WALLSMATERIAL_MODE | EMERGENCYSTATE_MODE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 100002 | 1 | Cash loans | M | N | Y | 0 | 202500.0 | 406597.5 | 24700.5 | 351000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.018801 | -9461 | -637 | -3648.0 | -2120 | NaN | 1 | 1 | 0 | 1 | 1 | 0 | Laborers | 1.0 | 2 | 2 | WEDNESDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.083037 | 0.262949 | 0.139376 | 0.0247 | 0.0369 | 0.9722 | 0.6192 | 0.0143 | 0.00 | 0.0690 | 0.0833 | 0.1250 | 0.0369 | 0.0202 | 0.0190 | 0.0000 | 0.0000 | 0.0252 | 0.0383 | 0.9722 | 0.6341 | 0.0144 | 0.0000 | 0.0690 | 0.0833 | 0.1250 | 0.0377 | 0.022 | 0.0198 | 0.0 | 0.0 | 0.0250 | 0.0369 | 0.9722 | 0.6243 | 0.0144 | 0.00 | 0.0690 | 0.0833 | 0.1250 | 0.0375 | 0.0205 | 0.0193 | 0.0000 | 0.00 | reg oper account | block of flats | 0.0149 | Stone, brick | No | 2.0 | 2.0 | 2.0 | 2.0 | -1134.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 1 | 100003 | 0 | Cash loans | F | N | N | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | -16765 | -1188 | -1186.0 | -291 | NaN | 1 | 1 | 0 | 1 | 1 | 0 | Core staff | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 0.311267 | 0.622246 | NaN | 0.0959 | 0.0529 | 0.9851 | 0.7960 | 0.0605 | 0.08 | 0.0345 | 0.2917 | 0.3333 | 0.0130 | 0.0773 | 0.0549 | 0.0039 | 0.0098 | 0.0924 | 0.0538 | 0.9851 | 0.8040 | 0.0497 | 0.0806 | 0.0345 | 0.2917 | 0.3333 | 0.0128 | 0.079 | 0.0554 | 0.0 | 0.0 | 0.0968 | 0.0529 | 0.9851 | 0.7987 | 0.0608 | 0.08 | 0.0345 | 0.2917 | 0.3333 | 0.0132 | 0.0787 | 0.0558 | 0.0039 | 0.01 | reg oper account | block of flats | 0.0714 | Block | No | 1.0 | 0.0 | 1.0 | 0.0 | -828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2 | 100004 | 0 | Revolving loans | M | Y | Y | 0 | 67500.0 | 135000.0 | 6750.0 | 135000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.010032 | -19046 | -225 | -4260.0 | -2531 | 26.0 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 1.0 | 2 | 2 | MONDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Government | NaN | 0.555912 | 0.729567 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -815.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
display("previous_application ")
display(previous_application.head(3))
'previous_application '
| SK_ID_PREV | SK_ID_CURR | NAME_CONTRACT_TYPE | AMT_ANNUITY | AMT_APPLICATION | AMT_CREDIT | AMT_DOWN_PAYMENT | AMT_GOODS_PRICE | WEEKDAY_APPR_PROCESS_START | HOUR_APPR_PROCESS_START | FLAG_LAST_APPL_PER_CONTRACT | NFLAG_LAST_APPL_IN_DAY | RATE_DOWN_PAYMENT | RATE_INTEREST_PRIMARY | RATE_INTEREST_PRIVILEGED | NAME_CASH_LOAN_PURPOSE | NAME_CONTRACT_STATUS | DAYS_DECISION | NAME_PAYMENT_TYPE | CODE_REJECT_REASON | NAME_TYPE_SUITE | NAME_CLIENT_TYPE | NAME_GOODS_CATEGORY | NAME_PORTFOLIO | NAME_PRODUCT_TYPE | CHANNEL_TYPE | SELLERPLACE_AREA | NAME_SELLER_INDUSTRY | CNT_PAYMENT | NAME_YIELD_GROUP | PRODUCT_COMBINATION | DAYS_FIRST_DRAWING | DAYS_FIRST_DUE | DAYS_LAST_DUE_1ST_VERSION | DAYS_LAST_DUE | DAYS_TERMINATION | NFLAG_INSURED_ON_APPROVAL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2030495 | 271877 | Consumer loans | 1730.430 | 17145.0 | 17145.0 | 0.0 | 17145.0 | SATURDAY | 15 | Y | 1 | 0.0 | 0.182832 | 0.867336 | XAP | Approved | -73 | Cash through the bank | XAP | NaN | Repeater | Mobile | POS | XNA | Country-wide | 35 | Connectivity | 12.0 | middle | POS mobile with interest | 365243.0 | -42.0 | 300.0 | -42.0 | -37.0 | 0.0 |
| 1 | 2802425 | 108129 | Cash loans | 25188.615 | 607500.0 | 679671.0 | NaN | 607500.0 | THURSDAY | 11 | Y | 1 | NaN | NaN | NaN | XNA | Approved | -164 | XNA | XAP | Unaccompanied | Repeater | XNA | Cash | x-sell | Contact center | -1 | XNA | 36.0 | low_action | Cash X-Sell: low | 365243.0 | -134.0 | 916.0 | 365243.0 | 365243.0 | 1.0 |
| 2 | 2523466 | 122040 | Cash loans | 15060.735 | 112500.0 | 136444.5 | NaN | 112500.0 | TUESDAY | 11 | Y | 1 | NaN | NaN | NaN | XNA | Approved | -301 | Cash through the bank | XAP | Spouse, partner | Repeater | XNA | Cash | x-sell | Credit and cash offices | -1 | XNA | 12.0 | high | Cash X-Sell: high | 365243.0 | -271.0 | 59.0 | 365243.0 | 365243.0 | 1.0 |
display("columns_description")
pd.set_option('display.max_colwidth', -1)
columns_description=columns_description.drop(['1'],axis=1)
display(columns_description)
'columns_description'
| application_data | SK_ID_CURR | ID of loan in our sample | Unnamed: 4 | |
|---|---|---|---|---|
| 0 | application_data | TARGET | Target variable (1 - client with payment difficulties: he/she had late payment more than X days on at least one of the first Y installments of the loan in our sample, 0 - all other cases) | NaN |
| 1 | application_data | NAME_CONTRACT_TYPE | Identification if loan is cash or revolving | NaN |
| 2 | application_data | CODE_GENDER | Gender of the client | NaN |
| 3 | application_data | FLAG_OWN_CAR | Flag if the client owns a car | NaN |
| 4 | application_data | FLAG_OWN_REALTY | Flag if client owns a house or flat | NaN |
| 5 | application_data | CNT_CHILDREN | Number of children the client has | NaN |
| 6 | application_data | AMT_INCOME_TOTAL | Income of the client | NaN |
| 7 | application_data | AMT_CREDIT | Credit amount of the loan | NaN |
| 8 | application_data | AMT_ANNUITY | Loan annuity | NaN |
| 9 | application_data | AMT_GOODS_PRICE | For consumer loans it is the price of the goods for which the loan is given | NaN |
| 10 | application_data | NAME_TYPE_SUITE | Who was accompanying client when he was applying for the loan | NaN |
| 11 | application_data | NAME_INCOME_TYPE | Clients income type (businessman, working, maternity leave,…) | NaN |
| 12 | application_data | NAME_EDUCATION_TYPE | Level of highest education the client achieved | NaN |
| 13 | application_data | NAME_FAMILY_STATUS | Family status of the client | NaN |
| 14 | application_data | NAME_HOUSING_TYPE | What is the housing situation of the client (renting, living with parents, ...) | NaN |
| 15 | application_data | REGION_POPULATION_RELATIVE | Normalized population of region where client lives (higher number means the client lives in more populated region) | normalized |
| 16 | application_data | DAYS_BIRTH | Client's age in days at the time of application | time only relative to the application |
| 17 | application_data | DAYS_EMPLOYED | How many days before the application the person started current employment | time only relative to the application |
| 18 | application_data | DAYS_REGISTRATION | How many days before the application did client change his registration | time only relative to the application |
| 19 | application_data | DAYS_ID_PUBLISH | How many days before the application did client change the identity document with which he applied for the loan | time only relative to the application |
| 20 | application_data | OWN_CAR_AGE | Age of client's car | NaN |
| 21 | application_data | FLAG_MOBIL | Did client provide mobile phone (1=YES, 0=NO) | NaN |
| 22 | application_data | FLAG_EMP_PHONE | Did client provide work phone (1=YES, 0=NO) | NaN |
| 23 | application_data | FLAG_WORK_PHONE | Did client provide home phone (1=YES, 0=NO) | NaN |
| 24 | application_data | FLAG_CONT_MOBILE | Was mobile phone reachable (1=YES, 0=NO) | NaN |
| 25 | application_data | FLAG_PHONE | Did client provide home phone (1=YES, 0=NO) | NaN |
| 26 | application_data | FLAG_EMAIL | Did client provide email (1=YES, 0=NO) | NaN |
| 27 | application_data | OCCUPATION_TYPE | What kind of occupation does the client have | NaN |
| 28 | application_data | CNT_FAM_MEMBERS | How many family members does client have | NaN |
| 29 | application_data | REGION_RATING_CLIENT | Our rating of the region where client lives (1,2,3) | NaN |
| 30 | application_data | REGION_RATING_CLIENT_W_CITY | Our rating of the region where client lives with taking city into account (1,2,3) | NaN |
| 31 | application_data | WEEKDAY_APPR_PROCESS_START | On which day of the week did the client apply for the loan | NaN |
| 32 | application_data | HOUR_APPR_PROCESS_START | Approximately at what hour did the client apply for the loan | rounded |
| 33 | application_data | REG_REGION_NOT_LIVE_REGION | Flag if client's permanent address does not match contact address (1=different, 0=same, at region level) | NaN |
| 34 | application_data | REG_REGION_NOT_WORK_REGION | Flag if client's permanent address does not match work address (1=different, 0=same, at region level) | NaN |
| 35 | application_data | LIVE_REGION_NOT_WORK_REGION | Flag if client's contact address does not match work address (1=different, 0=same, at region level) | NaN |
| 36 | application_data | REG_CITY_NOT_LIVE_CITY | Flag if client's permanent address does not match contact address (1=different, 0=same, at city level) | NaN |
| 37 | application_data | REG_CITY_NOT_WORK_CITY | Flag if client's permanent address does not match work address (1=different, 0=same, at city level) | NaN |
| 38 | application_data | LIVE_CITY_NOT_WORK_CITY | Flag if client's contact address does not match work address (1=different, 0=same, at city level) | NaN |
| 39 | application_data | ORGANIZATION_TYPE | Type of organization where client works | NaN |
| 40 | application_data | EXT_SOURCE_1 | Normalized score from external data source | normalized |
| 41 | application_data | EXT_SOURCE_2 | Normalized score from external data source | normalized |
| 42 | application_data | EXT_SOURCE_3 | Normalized score from external data source | normalized |
| 43 | application_data | APARTMENTS_AVG | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 44 | application_data | BASEMENTAREA_AVG | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 45 | application_data | YEARS_BEGINEXPLUATATION_AVG | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 46 | application_data | YEARS_BUILD_AVG | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 47 | application_data | COMMONAREA_AVG | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 48 | application_data | ELEVATORS_AVG | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 49 | application_data | ENTRANCES_AVG | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 50 | application_data | FLOORSMAX_AVG | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 51 | application_data | FLOORSMIN_AVG | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 52 | application_data | LANDAREA_AVG | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 53 | application_data | LIVINGAPARTMENTS_AVG | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 54 | application_data | LIVINGAREA_AVG | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 55 | application_data | NONLIVINGAPARTMENTS_AVG | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 56 | application_data | NONLIVINGAREA_AVG | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 57 | application_data | APARTMENTS_MODE | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 58 | application_data | BASEMENTAREA_MODE | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 59 | application_data | YEARS_BEGINEXPLUATATION_MODE | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 60 | application_data | YEARS_BUILD_MODE | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 61 | application_data | COMMONAREA_MODE | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 62 | application_data | ELEVATORS_MODE | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 63 | application_data | ENTRANCES_MODE | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 64 | application_data | FLOORSMAX_MODE | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 65 | application_data | FLOORSMIN_MODE | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 66 | application_data | LANDAREA_MODE | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 67 | application_data | LIVINGAPARTMENTS_MODE | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 68 | application_data | LIVINGAREA_MODE | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 69 | application_data | NONLIVINGAPARTMENTS_MODE | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 70 | application_data | NONLIVINGAREA_MODE | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 71 | application_data | APARTMENTS_MEDI | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 72 | application_data | BASEMENTAREA_MEDI | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 73 | application_data | YEARS_BEGINEXPLUATATION_MEDI | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 74 | application_data | YEARS_BUILD_MEDI | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 75 | application_data | COMMONAREA_MEDI | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 76 | application_data | ELEVATORS_MEDI | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 77 | application_data | ENTRANCES_MEDI | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 78 | application_data | FLOORSMAX_MEDI | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 79 | application_data | FLOORSMIN_MEDI | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 80 | application_data | LANDAREA_MEDI | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 81 | application_data | LIVINGAPARTMENTS_MEDI | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 82 | application_data | LIVINGAREA_MEDI | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 83 | application_data | NONLIVINGAPARTMENTS_MEDI | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 84 | application_data | NONLIVINGAREA_MEDI | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 85 | application_data | FONDKAPREMONT_MODE | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 86 | application_data | HOUSETYPE_MODE | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 87 | application_data | TOTALAREA_MODE | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 88 | application_data | WALLSMATERIAL_MODE | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 89 | application_data | EMERGENCYSTATE_MODE | Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor | normalized |
| 90 | application_data | OBS_30_CNT_SOCIAL_CIRCLE | How many observation of client's social surroundings with observable 30 DPD (days past due) default | NaN |
| 91 | application_data | DEF_30_CNT_SOCIAL_CIRCLE | How many observation of client's social surroundings defaulted on 30 DPD (days past due) | NaN |
| 92 | application_data | OBS_60_CNT_SOCIAL_CIRCLE | How many observation of client's social surroundings with observable 60 DPD (days past due) default | NaN |
| 93 | application_data | DEF_60_CNT_SOCIAL_CIRCLE | How many observation of client's social surroundings defaulted on 60 (days past due) DPD | NaN |
| 94 | application_data | DAYS_LAST_PHONE_CHANGE | How many days before application did client change phone | NaN |
| 95 | application_data | FLAG_DOCUMENT_2 | Did client provide document 2 | NaN |
| 96 | application_data | FLAG_DOCUMENT_3 | Did client provide document 3 | NaN |
| 97 | application_data | FLAG_DOCUMENT_4 | Did client provide document 4 | NaN |
| 98 | application_data | FLAG_DOCUMENT_5 | Did client provide document 5 | NaN |
| 99 | application_data | FLAG_DOCUMENT_6 | Did client provide document 6 | NaN |
| 100 | application_data | FLAG_DOCUMENT_7 | Did client provide document 7 | NaN |
| 101 | application_data | FLAG_DOCUMENT_8 | Did client provide document 8 | NaN |
| 102 | application_data | FLAG_DOCUMENT_9 | Did client provide document 9 | NaN |
| 103 | application_data | FLAG_DOCUMENT_10 | Did client provide document 10 | NaN |
| 104 | application_data | FLAG_DOCUMENT_11 | Did client provide document 11 | NaN |
| 105 | application_data | FLAG_DOCUMENT_12 | Did client provide document 12 | NaN |
| 106 | application_data | FLAG_DOCUMENT_13 | Did client provide document 13 | NaN |
| 107 | application_data | FLAG_DOCUMENT_14 | Did client provide document 14 | NaN |
| 108 | application_data | FLAG_DOCUMENT_15 | Did client provide document 15 | NaN |
| 109 | application_data | FLAG_DOCUMENT_16 | Did client provide document 16 | NaN |
| 110 | application_data | FLAG_DOCUMENT_17 | Did client provide document 17 | NaN |
| 111 | application_data | FLAG_DOCUMENT_18 | Did client provide document 18 | NaN |
| 112 | application_data | FLAG_DOCUMENT_19 | Did client provide document 19 | NaN |
| 113 | application_data | FLAG_DOCUMENT_20 | Did client provide document 20 | NaN |
| 114 | application_data | FLAG_DOCUMENT_21 | Did client provide document 21 | NaN |
| 115 | application_data | AMT_REQ_CREDIT_BUREAU_HOUR | Number of enquiries to Credit Bureau about the client one hour before application | NaN |
| 116 | application_data | AMT_REQ_CREDIT_BUREAU_DAY | Number of enquiries to Credit Bureau about the client one day before application (excluding one hour before application) | NaN |
| 117 | application_data | AMT_REQ_CREDIT_BUREAU_WEEK | Number of enquiries to Credit Bureau about the client one week before application (excluding one day before application) | NaN |
| 118 | application_data | AMT_REQ_CREDIT_BUREAU_MON | Number of enquiries to Credit Bureau about the client one month before application (excluding one week before application) | NaN |
| 119 | application_data | AMT_REQ_CREDIT_BUREAU_QRT | Number of enquiries to Credit Bureau about the client 3 month before application (excluding one month before application) | NaN |
| 120 | application_data | AMT_REQ_CREDIT_BUREAU_YEAR | Number of enquiries to Credit Bureau about the client one day year (excluding last 3 months before application) | NaN |
| 121 | previous_application.csv | SK_ID_PREV | ID of previous credit in Home credit related to loan in our sample. (One loan in our sample can have 0,1,2 or more previous loan applications in Home Credit, previous application could, but not necessarily have to lead to credit) | hashed |
| 122 | previous_application.csv | SK_ID_CURR | ID of loan in our sample | hashed |
| 123 | previous_application.csv | NAME_CONTRACT_TYPE | Contract product type (Cash loan, consumer loan [POS] ,...) of the previous application | NaN |
| 124 | previous_application.csv | AMT_ANNUITY | Annuity of previous application | NaN |
| 125 | previous_application.csv | AMT_APPLICATION | For how much credit did client ask on the previous application | NaN |
| 126 | previous_application.csv | AMT_CREDIT | Final credit amount on the previous application. This differs from AMT_APPLICATION in a way that the AMT_APPLICATION is the amount for which the client initially applied for, but during our approval process he could have received different amount - AMT_CREDIT | NaN |
| 127 | previous_application.csv | AMT_DOWN_PAYMENT | Down payment on the previous application | NaN |
| 128 | previous_application.csv | AMT_GOODS_PRICE | Goods price of good that client asked for (if applicable) on the previous application | NaN |
| 129 | previous_application.csv | WEEKDAY_APPR_PROCESS_START | On which day of the week did the client apply for previous application | NaN |
| 130 | previous_application.csv | HOUR_APPR_PROCESS_START | Approximately at what day hour did the client apply for the previous application | rounded |
| 131 | previous_application.csv | FLAG_LAST_APPL_PER_CONTRACT | Flag if it was last application for the previous contract. Sometimes by mistake of client or our clerk there could be more applications for one single contract | NaN |
| 132 | previous_application.csv | NFLAG_LAST_APPL_IN_DAY | Flag if the application was the last application per day of the client. Sometimes clients apply for more applications a day. Rarely it could also be error in our system that one application is in the database twice | NaN |
| 133 | previous_application.csv | NFLAG_MICRO_CASH | Flag Micro finance loan | NaN |
| 134 | previous_application.csv | RATE_DOWN_PAYMENT | Down payment rate normalized on previous credit | normalized |
| 135 | previous_application.csv | RATE_INTEREST_PRIMARY | Interest rate normalized on previous credit | normalized |
| 136 | previous_application.csv | RATE_INTEREST_PRIVILEGED | Interest rate normalized on previous credit | normalized |
| 137 | previous_application.csv | NAME_CASH_LOAN_PURPOSE | Purpose of the cash loan | NaN |
| 138 | previous_application.csv | NAME_CONTRACT_STATUS | Contract status (approved, cancelled, ...) of previous application | NaN |
| 139 | previous_application.csv | DAYS_DECISION | Relative to current application when was the decision about previous application made | time only relative to the application |
| 140 | previous_application.csv | NAME_PAYMENT_TYPE | Payment method that client chose to pay for the previous application | NaN |
| 141 | previous_application.csv | CODE_REJECT_REASON | Why was the previous application rejected | NaN |
| 142 | previous_application.csv | NAME_TYPE_SUITE | Who accompanied client when applying for the previous application | NaN |
| 143 | previous_application.csv | NAME_CLIENT_TYPE | Was the client old or new client when applying for the previous application | NaN |
| 144 | previous_application.csv | NAME_GOODS_CATEGORY | What kind of goods did the client apply for in the previous application | NaN |
| 145 | previous_application.csv | NAME_PORTFOLIO | Was the previous application for CASH, POS, CAR, … | NaN |
| 146 | previous_application.csv | NAME_PRODUCT_TYPE | Was the previous application x-sell o walk-in | NaN |
| 147 | previous_application.csv | CHANNEL_TYPE | Through which channel we acquired the client on the previous application | NaN |
| 148 | previous_application.csv | SELLERPLACE_AREA | Selling area of seller place of the previous application | NaN |
| 149 | previous_application.csv | NAME_SELLER_INDUSTRY | The industry of the seller | NaN |
| 150 | previous_application.csv | CNT_PAYMENT | Term of previous credit at application of the previous application | NaN |
| 151 | previous_application.csv | NAME_YIELD_GROUP | Grouped interest rate into small medium and high of the previous application | grouped |
| 152 | previous_application.csv | PRODUCT_COMBINATION | Detailed product combination of the previous application | NaN |
| 153 | previous_application.csv | DAYS_FIRST_DRAWING | Relative to application date of current application when was the first disbursement of the previous application | time only relative to the application |
| 154 | previous_application.csv | DAYS_FIRST_DUE | Relative to application date of current application when was the first due supposed to be of the previous application | time only relative to the application |
| 155 | previous_application.csv | DAYS_LAST_DUE_1ST_VERSION | Relative to application date of current application when was the first due of the previous application | time only relative to the application |
| 156 | previous_application.csv | DAYS_LAST_DUE | Relative to application date of current application when was the last due date of the previous application | time only relative to the application |
| 157 | previous_application.csv | DAYS_TERMINATION | Relative to application date of current application when was the expected termination of the previous application | time only relative to the application |
| 158 | previous_application.csv | NFLAG_INSURED_ON_APPROVAL | Did the client requested insurance during the previous application | NaN |
fig = plt.figure(figsize=(18,6))
miss_previous_application = pd.DataFrame((previous_application.isnull().sum())*100/previous_application.shape[0]).reset_index()
miss_previous_application["type"] = "previous_application"
ax = sns.pointplot("index",0,data=miss_previous_application,hue="type")
plt.xticks(rotation =90,fontsize =7)
plt.title("Percentage of Missing values in previous_application")
plt.ylabel("PERCENTAGE")
plt.xlabel("COLUMNS")
ax.set_facecolor("k")
fig.set_facecolor("lightgrey")
round(100*(previous_application.isnull().sum()/len(previous_application.index)),2)
SK_ID_PREV 0.00 SK_ID_CURR 0.00 NAME_CONTRACT_TYPE 0.00 AMT_ANNUITY 22.29 AMT_APPLICATION 0.00 AMT_CREDIT 0.00 AMT_DOWN_PAYMENT 53.64 AMT_GOODS_PRICE 23.08 WEEKDAY_APPR_PROCESS_START 0.00 HOUR_APPR_PROCESS_START 0.00 FLAG_LAST_APPL_PER_CONTRACT 0.00 NFLAG_LAST_APPL_IN_DAY 0.00 RATE_DOWN_PAYMENT 53.64 RATE_INTEREST_PRIMARY 99.64 RATE_INTEREST_PRIVILEGED 99.64 NAME_CASH_LOAN_PURPOSE 0.00 NAME_CONTRACT_STATUS 0.00 DAYS_DECISION 0.00 NAME_PAYMENT_TYPE 0.00 CODE_REJECT_REASON 0.00 NAME_TYPE_SUITE 49.12 NAME_CLIENT_TYPE 0.00 NAME_GOODS_CATEGORY 0.00 NAME_PORTFOLIO 0.00 NAME_PRODUCT_TYPE 0.00 CHANNEL_TYPE 0.00 SELLERPLACE_AREA 0.00 NAME_SELLER_INDUSTRY 0.00 CNT_PAYMENT 22.29 NAME_YIELD_GROUP 0.00 PRODUCT_COMBINATION 0.02 DAYS_FIRST_DRAWING 40.30 DAYS_FIRST_DUE 40.30 DAYS_LAST_DUE_1ST_VERSION 40.30 DAYS_LAST_DUE 40.30 DAYS_TERMINATION 40.30 NFLAG_INSURED_ON_APPROVAL 40.30 dtype: float64
previous_application=previous_application.drop([ 'AMT_DOWN_PAYMENT', 'RATE_DOWN_PAYMENT', 'RATE_INTEREST_PRIMARY',
"RATE_INTEREST_PRIVILEGED"],axis=1)
fig = plt.figure(figsize=(18,6))
miss_previous_application = pd.DataFrame((previous_application.isnull().sum())*100/previous_application.shape[0]).reset_index()
miss_previous_application["type"] = "previous_application"
ax = sns.pointplot("index",0,data=miss_previous_application,hue="type")
plt.xticks(rotation =90,fontsize =7)
plt.title("Percentage of Missing values in previous_application")
plt.ylabel("PERCENTAGE")
plt.xlabel("COLUMNS")
ax.set_facecolor("k")
fig.set_facecolor("lightgrey")
round(100*(previous_application.isnull().sum()/len(previous_application.index)),2)
SK_ID_PREV 0.00 SK_ID_CURR 0.00 NAME_CONTRACT_TYPE 0.00 AMT_ANNUITY 22.29 AMT_APPLICATION 0.00 AMT_CREDIT 0.00 AMT_GOODS_PRICE 23.08 WEEKDAY_APPR_PROCESS_START 0.00 HOUR_APPR_PROCESS_START 0.00 FLAG_LAST_APPL_PER_CONTRACT 0.00 NFLAG_LAST_APPL_IN_DAY 0.00 NAME_CASH_LOAN_PURPOSE 0.00 NAME_CONTRACT_STATUS 0.00 DAYS_DECISION 0.00 NAME_PAYMENT_TYPE 0.00 CODE_REJECT_REASON 0.00 NAME_TYPE_SUITE 49.12 NAME_CLIENT_TYPE 0.00 NAME_GOODS_CATEGORY 0.00 NAME_PORTFOLIO 0.00 NAME_PRODUCT_TYPE 0.00 CHANNEL_TYPE 0.00 SELLERPLACE_AREA 0.00 NAME_SELLER_INDUSTRY 0.00 CNT_PAYMENT 22.29 NAME_YIELD_GROUP 0.00 PRODUCT_COMBINATION 0.02 DAYS_FIRST_DRAWING 40.30 DAYS_FIRST_DUE 40.30 DAYS_LAST_DUE_1ST_VERSION 40.30 DAYS_LAST_DUE 40.30 DAYS_TERMINATION 40.30 NFLAG_INSURED_ON_APPROVAL 40.30 dtype: float64
print("AMT_ANNUITY NULL COUNT:" ,previous_application['AMT_ANNUITY'].isnull().sum())
AMT_ANNUITY NULL COUNT: 372235
previous_application['AMT_ANNUITY'].describe()
count 1.297979e+06 mean 1.595512e+04 std 1.478214e+04 min 0.000000e+00 25% 6.321780e+03 50% 1.125000e+04 75% 2.065842e+04 max 4.180581e+05 Name: AMT_ANNUITY, dtype: float64
sns.set_style('whitegrid')
sns.distplot(previous_application['AMT_ANNUITY'])
plt.show()
print("AMT_GOODS_PRICE NULL COUNT:" ,previous_application['AMT_GOODS_PRICE'].isnull().sum())
AMT_GOODS_PRICE NULL COUNT: 385515
previous_application['AMT_GOODS_PRICE'].describe()
count 1.284699e+06 mean 2.278473e+05 std 3.153966e+05 min 0.000000e+00 25% 5.084100e+04 50% 1.123200e+05 75% 2.340000e+05 max 6.905160e+06 Name: AMT_GOODS_PRICE, dtype: float64
sns.set_style('whitegrid')
sns.distplot(previous_application['AMT_GOODS_PRICE'])
plt.show()
print("NAME_TYPE_SUITE NULL COUNT:" ,previous_application['NAME_TYPE_SUITE'].isnull().sum())
NAME_TYPE_SUITE NULL COUNT: 820405
previous_application['NAME_TYPE_SUITE'].value_counts()
Unaccompanied 508970 Family 213263 Spouse, partner 67069 Children 31566 Other_B 17624 Other_A 9077 Group of people 2240 Name: NAME_TYPE_SUITE, dtype: int64
print("CNT_PAYMENT NULL COUNT:" ,previous_application['CNT_PAYMENT'].isnull().sum())
CNT_PAYMENT NULL COUNT: 372230
previous_application['CNT_PAYMENT'].describe()
count 1.297984e+06 mean 1.605408e+01 std 1.456729e+01 min 0.000000e+00 25% 6.000000e+00 50% 1.200000e+01 75% 2.400000e+01 max 8.400000e+01 Name: CNT_PAYMENT, dtype: float64
sns.set_style('whitegrid')
sns.boxplot(previous_application['CNT_PAYMENT'])
plt.show()
print("DAYS_FIRST_DRAWING :" ,previous_application['CNT_PAYMENT'].isnull().sum())
DAYS_FIRST_DRAWING : 372230
previous_application['DAYS_FIRST_DRAWING'].describe()
count 997149.000000 mean 342209.855039 std 88916.115834 min -2922.000000 25% 365243.000000 50% 365243.000000 75% 365243.000000 max 365243.000000 Name: DAYS_FIRST_DRAWING, dtype: float64
sns.set_style('whitegrid')
sns.boxplot(previous_application['DAYS_FIRST_DRAWING'])
plt.show()
print("DAYS_FIRST_DUE :" ,previous_application['DAYS_FIRST_DUE'].isnull().sum())
DAYS_FIRST_DUE : 673065
previous_application['DAYS_FIRST_DUE'].describe()
count 997149.000000 mean 13826.269337 std 72444.869708 min -2892.000000 25% -1628.000000 50% -831.000000 75% -411.000000 max 365243.000000 Name: DAYS_FIRST_DUE, dtype: float64
sns.set_style('whitegrid')
sns.boxplot(previous_application['DAYS_FIRST_DUE'])
plt.show()
print("DAYS_LAST_DUE_1ST_VERSION :" ,previous_application['DAYS_LAST_DUE_1ST_VERSION'].isnull().sum())
DAYS_LAST_DUE_1ST_VERSION : 673065
previous_application['DAYS_LAST_DUE_1ST_VERSION'].describe()
count 997149.000000 mean 33767.774054 std 106857.034789 min -2801.000000 25% -1242.000000 50% -361.000000 75% 129.000000 max 365243.000000 Name: DAYS_LAST_DUE_1ST_VERSION, dtype: float64
sns.set_style('whitegrid')
sns.boxplot(previous_application['DAYS_LAST_DUE_1ST_VERSION'])
plt.show()
print("DAYS_LAST_DUE:" ,previous_application['DAYS_LAST_DUE'].isnull().sum())
DAYS_LAST_DUE: 673065
previous_application['DAYS_LAST_DUE'].describe()
count 997149.000000 mean 76582.403064 std 149647.415123 min -2889.000000 25% -1314.000000 50% -537.000000 75% -74.000000 max 365243.000000 Name: DAYS_LAST_DUE, dtype: float64
sns.set_style('whitegrid')
sns.boxplot(previous_application['DAYS_LAST_DUE'])
plt.show()
print("DAYS_TERMINATION :" ,previous_application['DAYS_TERMINATION'].isnull().sum())
DAYS_TERMINATION : 673065
previous_application['DAYS_TERMINATION'].describe()
count 997149.000000 mean 81992.343838 std 153303.516729 min -2874.000000 25% -1270.000000 50% -499.000000 75% -44.000000 max 365243.000000 Name: DAYS_TERMINATION, dtype: float64
sns.set_style('whitegrid')
sns.boxplot(previous_application['DAYS_TERMINATION'])
plt.show()
print("NFLAG_INSURED_ON_APPROVAL:" ,previous_application['NFLAG_INSURED_ON_APPROVAL'].isnull().sum())
NFLAG_INSURED_ON_APPROVAL: 673065
previous_application['NFLAG_INSURED_ON_APPROVAL'].value_counts()
0.0 665527 1.0 331622 Name: NFLAG_INSURED_ON_APPROVAL, dtype: int64
previous_application.isnull().sum()
SK_ID_PREV 0 SK_ID_CURR 0 NAME_CONTRACT_TYPE 0 AMT_ANNUITY 372235 AMT_APPLICATION 0 AMT_CREDIT 1 AMT_GOODS_PRICE 385515 WEEKDAY_APPR_PROCESS_START 0 HOUR_APPR_PROCESS_START 0 FLAG_LAST_APPL_PER_CONTRACT 0 NFLAG_LAST_APPL_IN_DAY 0 NAME_CASH_LOAN_PURPOSE 0 NAME_CONTRACT_STATUS 0 DAYS_DECISION 0 NAME_PAYMENT_TYPE 0 CODE_REJECT_REASON 0 NAME_TYPE_SUITE 820405 NAME_CLIENT_TYPE 0 NAME_GOODS_CATEGORY 0 NAME_PORTFOLIO 0 NAME_PRODUCT_TYPE 0 CHANNEL_TYPE 0 SELLERPLACE_AREA 0 NAME_SELLER_INDUSTRY 0 CNT_PAYMENT 372230 NAME_YIELD_GROUP 0 PRODUCT_COMBINATION 346 DAYS_FIRST_DRAWING 673065 DAYS_FIRST_DUE 673065 DAYS_LAST_DUE_1ST_VERSION 673065 DAYS_LAST_DUE 673065 DAYS_TERMINATION 673065 NFLAG_INSURED_ON_APPROVAL 673065 dtype: int64
print("AMT_CREDIT :" ,previous_application['AMT_CREDIT'].isnull().sum())
AMT_CREDIT : 1
previous_application['AMT_CREDIT'].describe()
count 1.670213e+06 mean 1.961140e+05 std 3.185746e+05 min 0.000000e+00 25% 2.416050e+04 50% 8.054100e+04 75% 2.164185e+05 max 6.905160e+06 Name: AMT_CREDIT, dtype: float64
sns.set_style('whitegrid')
sns.boxplot(previous_application['AMT_CREDIT'])
plt.show()
print("PRODUCT_COMBINATION :" ,previous_application['PRODUCT_COMBINATION'].isnull().sum())
PRODUCT_COMBINATION : 346
previous_application['PRODUCT_COMBINATION'].value_counts()
Cash 285990 POS household with interest 263622 POS mobile with interest 220670 Cash X-Sell: middle 143883 Cash X-Sell: low 130248 Card Street 112582 POS industry with interest 98833 POS household without interest 82908 Card X-Sell 80582 Cash Street: high 59639 Cash X-Sell: high 59301 Cash Street: middle 34658 Cash Street: low 33834 POS mobile without interest 24082 POS other with interest 23879 POS industry without interest 12602 POS others without interest 2555 Name: PRODUCT_COMBINATION, dtype: int64
class color:
PURPLE = '\033[95m'
CYAN = '\033[96m'
DARKCYAN = '\033[36m'
BLUE = '\033[94m'
GREEN = '\033[92m'
YELLOW = '\033[93m'
RED = '\033[91m'
BOLD = '\033[1m'
UNDERLINE = '\033[4m'
END = '\033[0m'
obj_dtypes = [i for i in previous_application.select_dtypes(include=object).columns if i not in ["type"] ]
num_dtypes = [i for i in previous_application.select_dtypes(include = np.number).columns if i not in ['SK_ID_CURR'] + [ 'TARGET']]
print(color.BOLD + color.PURPLE + 'Categorical Columns' + color.END, "\n")
for x in range(len(obj_dtypes)):
print(obj_dtypes[x])
Categorical Columns
NAME_CONTRACT_TYPE
WEEKDAY_APPR_PROCESS_START
FLAG_LAST_APPL_PER_CONTRACT
NAME_CASH_LOAN_PURPOSE
NAME_CONTRACT_STATUS
NAME_PAYMENT_TYPE
CODE_REJECT_REASON
NAME_TYPE_SUITE
NAME_CLIENT_TYPE
NAME_GOODS_CATEGORY
NAME_PORTFOLIO
NAME_PRODUCT_TYPE
CHANNEL_TYPE
NAME_SELLER_INDUSTRY
NAME_YIELD_GROUP
PRODUCT_COMBINATION
print(color.BOLD + color.PURPLE + 'Numerical' + color.END, "\n")
for x in range(len(obj_dtypes)):
print(obj_dtypes[x])
Numerical
NAME_CONTRACT_TYPE
WEEKDAY_APPR_PROCESS_START
FLAG_LAST_APPL_PER_CONTRACT
NAME_CASH_LOAN_PURPOSE
NAME_CONTRACT_STATUS
NAME_PAYMENT_TYPE
CODE_REJECT_REASON
NAME_TYPE_SUITE
NAME_CLIENT_TYPE
NAME_GOODS_CATEGORY
NAME_PORTFOLIO
NAME_PRODUCT_TYPE
CHANNEL_TYPE
NAME_SELLER_INDUSTRY
NAME_YIELD_GROUP
PRODUCT_COMBINATION
fig = plt.figure(figsize=(18,6))
miss_application_data = pd.DataFrame((application_data.isnull().sum())*100/application_data.shape[0]).reset_index()
miss_application_data["type"] = "application_data"
ax = sns.pointplot("index",0,data=miss_application_data,hue="type")
plt.xticks(rotation =90,fontsize =7)
plt.title("Percentage of Missing values in application_data")
plt.ylabel("PERCENTAGE")
plt.xlabel("COLUMNS")
ax.set_facecolor("k")
fig.set_facecolor("lightgrey")
round(100*(application_data.isnull().sum()/len(application_data.index)),2)
SK_ID_CURR 0.00 TARGET 0.00 NAME_CONTRACT_TYPE 0.00 CODE_GENDER 0.00 FLAG_OWN_CAR 0.00 FLAG_OWN_REALTY 0.00 CNT_CHILDREN 0.00 AMT_INCOME_TOTAL 0.00 AMT_CREDIT 0.00 AMT_ANNUITY 0.00 AMT_GOODS_PRICE 0.09 NAME_TYPE_SUITE 0.42 NAME_INCOME_TYPE 0.00 NAME_EDUCATION_TYPE 0.00 NAME_FAMILY_STATUS 0.00 NAME_HOUSING_TYPE 0.00 REGION_POPULATION_RELATIVE 0.00 DAYS_BIRTH 0.00 DAYS_EMPLOYED 0.00 DAYS_REGISTRATION 0.00 DAYS_ID_PUBLISH 0.00 OWN_CAR_AGE 65.99 FLAG_MOBIL 0.00 FLAG_EMP_PHONE 0.00 FLAG_WORK_PHONE 0.00 FLAG_CONT_MOBILE 0.00 FLAG_PHONE 0.00 FLAG_EMAIL 0.00 OCCUPATION_TYPE 31.35 CNT_FAM_MEMBERS 0.00 REGION_RATING_CLIENT 0.00 REGION_RATING_CLIENT_W_CITY 0.00 WEEKDAY_APPR_PROCESS_START 0.00 HOUR_APPR_PROCESS_START 0.00 REG_REGION_NOT_LIVE_REGION 0.00 REG_REGION_NOT_WORK_REGION 0.00 LIVE_REGION_NOT_WORK_REGION 0.00 REG_CITY_NOT_LIVE_CITY 0.00 REG_CITY_NOT_WORK_CITY 0.00 LIVE_CITY_NOT_WORK_CITY 0.00 ORGANIZATION_TYPE 0.00 EXT_SOURCE_1 56.38 EXT_SOURCE_2 0.21 EXT_SOURCE_3 19.83 APARTMENTS_AVG 50.75 BASEMENTAREA_AVG 58.52 YEARS_BEGINEXPLUATATION_AVG 48.78 YEARS_BUILD_AVG 66.50 COMMONAREA_AVG 69.87 ELEVATORS_AVG 53.30 ENTRANCES_AVG 50.35 FLOORSMAX_AVG 49.76 FLOORSMIN_AVG 67.85 LANDAREA_AVG 59.38 LIVINGAPARTMENTS_AVG 68.35 LIVINGAREA_AVG 50.19 NONLIVINGAPARTMENTS_AVG 69.43 NONLIVINGAREA_AVG 55.18 APARTMENTS_MODE 50.75 BASEMENTAREA_MODE 58.52 YEARS_BEGINEXPLUATATION_MODE 48.78 YEARS_BUILD_MODE 66.50 COMMONAREA_MODE 69.87 ELEVATORS_MODE 53.30 ENTRANCES_MODE 50.35 FLOORSMAX_MODE 49.76 FLOORSMIN_MODE 67.85 LANDAREA_MODE 59.38 LIVINGAPARTMENTS_MODE 68.35 LIVINGAREA_MODE 50.19 NONLIVINGAPARTMENTS_MODE 69.43 NONLIVINGAREA_MODE 55.18 APARTMENTS_MEDI 50.75 BASEMENTAREA_MEDI 58.52 YEARS_BEGINEXPLUATATION_MEDI 48.78 YEARS_BUILD_MEDI 66.50 COMMONAREA_MEDI 69.87 ELEVATORS_MEDI 53.30 ENTRANCES_MEDI 50.35 FLOORSMAX_MEDI 49.76 FLOORSMIN_MEDI 67.85 LANDAREA_MEDI 59.38 LIVINGAPARTMENTS_MEDI 68.35 LIVINGAREA_MEDI 50.19 NONLIVINGAPARTMENTS_MEDI 69.43 NONLIVINGAREA_MEDI 55.18 FONDKAPREMONT_MODE 68.39 HOUSETYPE_MODE 50.18 TOTALAREA_MODE 48.27 WALLSMATERIAL_MODE 50.84 EMERGENCYSTATE_MODE 47.40 OBS_30_CNT_SOCIAL_CIRCLE 0.33 DEF_30_CNT_SOCIAL_CIRCLE 0.33 OBS_60_CNT_SOCIAL_CIRCLE 0.33 DEF_60_CNT_SOCIAL_CIRCLE 0.33 DAYS_LAST_PHONE_CHANGE 0.00 FLAG_DOCUMENT_2 0.00 FLAG_DOCUMENT_3 0.00 FLAG_DOCUMENT_4 0.00 FLAG_DOCUMENT_5 0.00 FLAG_DOCUMENT_6 0.00 FLAG_DOCUMENT_7 0.00 FLAG_DOCUMENT_8 0.00 FLAG_DOCUMENT_9 0.00 FLAG_DOCUMENT_10 0.00 FLAG_DOCUMENT_11 0.00 FLAG_DOCUMENT_12 0.00 FLAG_DOCUMENT_13 0.00 FLAG_DOCUMENT_14 0.00 FLAG_DOCUMENT_15 0.00 FLAG_DOCUMENT_16 0.00 FLAG_DOCUMENT_17 0.00 FLAG_DOCUMENT_18 0.00 FLAG_DOCUMENT_19 0.00 FLAG_DOCUMENT_20 0.00 FLAG_DOCUMENT_21 0.00 AMT_REQ_CREDIT_BUREAU_HOUR 13.50 AMT_REQ_CREDIT_BUREAU_DAY 13.50 AMT_REQ_CREDIT_BUREAU_WEEK 13.50 AMT_REQ_CREDIT_BUREAU_MON 13.50 AMT_REQ_CREDIT_BUREAU_QRT 13.50 AMT_REQ_CREDIT_BUREAU_YEAR 13.50 dtype: float64
fig = plt.figure(figsize=(18,6))
miss_application_data = pd.DataFrame((application_data.isnull().sum())*100/application_data.shape[0]).reset_index()
miss_application_data["type"] = "application_data"
ax = sns.pointplot("index",0,data=miss_application_data,hue="type")
plt.xticks(rotation =90,fontsize =7)
plt.title("Percentage of Missing values in application_data")
plt.ylabel("PERCENTAGE")
plt.xlabel("COLUMNS")
ax.set_facecolor("k")
fig.set_facecolor("lightgrey")
round(100*(application_data.isnull().sum()/len(application_data.index)),2)
SK_ID_CURR 0.00 TARGET 0.00 NAME_CONTRACT_TYPE 0.00 CODE_GENDER 0.00 FLAG_OWN_CAR 0.00 FLAG_OWN_REALTY 0.00 CNT_CHILDREN 0.00 AMT_INCOME_TOTAL 0.00 AMT_CREDIT 0.00 AMT_ANNUITY 0.00 AMT_GOODS_PRICE 0.09 NAME_TYPE_SUITE 0.42 NAME_INCOME_TYPE 0.00 NAME_EDUCATION_TYPE 0.00 NAME_FAMILY_STATUS 0.00 NAME_HOUSING_TYPE 0.00 REGION_POPULATION_RELATIVE 0.00 DAYS_BIRTH 0.00 DAYS_EMPLOYED 0.00 DAYS_REGISTRATION 0.00 DAYS_ID_PUBLISH 0.00 FLAG_MOBIL 0.00 FLAG_EMP_PHONE 0.00 FLAG_WORK_PHONE 0.00 FLAG_CONT_MOBILE 0.00 FLAG_PHONE 0.00 FLAG_EMAIL 0.00 CNT_FAM_MEMBERS 0.00 REGION_RATING_CLIENT 0.00 REGION_RATING_CLIENT_W_CITY 0.00 WEEKDAY_APPR_PROCESS_START 0.00 HOUR_APPR_PROCESS_START 0.00 REG_REGION_NOT_LIVE_REGION 0.00 REG_REGION_NOT_WORK_REGION 0.00 LIVE_REGION_NOT_WORK_REGION 0.00 REG_CITY_NOT_LIVE_CITY 0.00 REG_CITY_NOT_WORK_CITY 0.00 LIVE_CITY_NOT_WORK_CITY 0.00 ORGANIZATION_TYPE 0.00 OBS_30_CNT_SOCIAL_CIRCLE 0.33 DEF_30_CNT_SOCIAL_CIRCLE 0.33 OBS_60_CNT_SOCIAL_CIRCLE 0.33 DEF_60_CNT_SOCIAL_CIRCLE 0.33 DAYS_LAST_PHONE_CHANGE 0.00 FLAG_DOCUMENT_2 0.00 FLAG_DOCUMENT_3 0.00 FLAG_DOCUMENT_4 0.00 FLAG_DOCUMENT_5 0.00 FLAG_DOCUMENT_6 0.00 FLAG_DOCUMENT_7 0.00 FLAG_DOCUMENT_8 0.00 FLAG_DOCUMENT_9 0.00 FLAG_DOCUMENT_10 0.00 FLAG_DOCUMENT_11 0.00 FLAG_DOCUMENT_12 0.00 FLAG_DOCUMENT_13 0.00 FLAG_DOCUMENT_14 0.00 FLAG_DOCUMENT_15 0.00 FLAG_DOCUMENT_16 0.00 FLAG_DOCUMENT_17 0.00 FLAG_DOCUMENT_18 0.00 FLAG_DOCUMENT_19 0.00 FLAG_DOCUMENT_20 0.00 FLAG_DOCUMENT_21 0.00 AMT_REQ_CREDIT_BUREAU_HOUR 13.50 AMT_REQ_CREDIT_BUREAU_DAY 13.50 AMT_REQ_CREDIT_BUREAU_WEEK 13.50 AMT_REQ_CREDIT_BUREAU_MON 13.50 AMT_REQ_CREDIT_BUREAU_QRT 13.50 AMT_REQ_CREDIT_BUREAU_YEAR 13.50 dtype: float64
print("AMT_REQ_CREDIT_BUREAU_DAY NAN COUNT :" ,application_data['AMT_REQ_CREDIT_BUREAU_DAY'].isnull().sum())
AMT_REQ_CREDIT_BUREAU_DAY NAN COUNT : 41519
application_data['AMT_REQ_CREDIT_BUREAU_DAY'].describe()
count 265992.000000 mean 0.007000 std 0.110757 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 9.000000 Name: AMT_REQ_CREDIT_BUREAU_DAY, dtype: float64
print("AMT_REQ_CREDIT_BUREAU_HOUR NAN COUNT :" ,application_data['AMT_REQ_CREDIT_BUREAU_HOUR'].isnull().sum())
AMT_REQ_CREDIT_BUREAU_HOUR NAN COUNT : 41519
application_data['AMT_REQ_CREDIT_BUREAU_HOUR'].describe()
count 265992.000000 mean 0.006402 std 0.083849 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 4.000000 Name: AMT_REQ_CREDIT_BUREAU_HOUR, dtype: float64
print("AMT_REQ_CREDIT_BUREAU_MON NAN COUNT :" ,application_data['AMT_REQ_CREDIT_BUREAU_MON'].isnull().sum())
AMT_REQ_CREDIT_BUREAU_MON NAN COUNT : 41519
application_data['AMT_REQ_CREDIT_BUREAU_MON'].describe()
count 265992.000000 mean 0.267395 std 0.916002 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 27.000000 Name: AMT_REQ_CREDIT_BUREAU_MON, dtype: float64
print("AMT_REQ_CREDIT_BUREAU_QRT NAN COUNT :" ,application_data['AMT_REQ_CREDIT_BUREAU_QRT'].isnull().sum())
AMT_REQ_CREDIT_BUREAU_QRT NAN COUNT : 41519
print("AMT_REQ_CREDIT_BUREAU_WEEK NAN COUNT :" ,application_data['AMT_REQ_CREDIT_BUREAU_WEEK'].isnull().sum())
AMT_REQ_CREDIT_BUREAU_WEEK NAN COUNT : 41519
application_data['AMT_REQ_CREDIT_BUREAU_WEEK'].describe()
count 265992.000000 mean 0.034362 std 0.204685 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 8.000000 Name: AMT_REQ_CREDIT_BUREAU_WEEK, dtype: float64
print("AMT_REQ_CREDIT_BUREAU_YEAR NAN COUNT :" ,application_data['AMT_REQ_CREDIT_BUREAU_YEAR'].isnull().sum())
AMT_REQ_CREDIT_BUREAU_YEAR NAN COUNT : 41519
application_data['AMT_REQ_CREDIT_BUREAU_YEAR'].describe()
count 265992.000000 mean 1.899974 std 1.869295 min 0.000000 25% 0.000000 50% 1.000000 75% 3.000000 max 25.000000 Name: AMT_REQ_CREDIT_BUREAU_YEAR, dtype: float64
print("DEF_30_CNT_SOCIAL_CIRCLE NAN COUNT :" ,application_data['DEF_30_CNT_SOCIAL_CIRCLE'].isnull().sum())
DEF_30_CNT_SOCIAL_CIRCLE NAN COUNT : 1021
application_data['DEF_30_CNT_SOCIAL_CIRCLE'].describe()
count 306490.000000 mean 0.143421 std 0.446698 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 34.000000 Name: DEF_30_CNT_SOCIAL_CIRCLE, dtype: float64
print("DEF_30_CNT_SOCIAL_CIRCLE :" ,application_data['DEF_30_CNT_SOCIAL_CIRCLE'].isnull().sum())
DEF_30_CNT_SOCIAL_CIRCLE : 1021
application_data['DEF_30_CNT_SOCIAL_CIRCLE'].describe()
count 306490.000000 mean 0.143421 std 0.446698 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 34.000000 Name: DEF_30_CNT_SOCIAL_CIRCLE, dtype: float64
print("OBS_60_CNT_SOCIAL_CIRCLE :" ,application_data['OBS_60_CNT_SOCIAL_CIRCLE'].isnull().sum())
OBS_60_CNT_SOCIAL_CIRCLE : 1021
application_data['OBS_60_CNT_SOCIAL_CIRCLE'].describe()
count 306490.000000 mean 1.405292 std 2.379803 min 0.000000 25% 0.000000 50% 0.000000 75% 2.000000 max 344.000000 Name: OBS_60_CNT_SOCIAL_CIRCLE, dtype: float64
print("DEF_60_CNT_SOCIAL_CIRCLE :" ,application_data['DEF_60_CNT_SOCIAL_CIRCLE'].isnull().sum())
DEF_60_CNT_SOCIAL_CIRCLE : 1021
application_data['DEF_60_CNT_SOCIAL_CIRCLE'].describe()
count 306490.000000 mean 0.100049 std 0.362291 min 0.000000 25% 0.000000 50% 0.000000 75% 0.000000 max 24.000000 Name: DEF_60_CNT_SOCIAL_CIRCLE, dtype: float64
application_data.isnull().sum()
SK_ID_CURR 0 TARGET 0 NAME_CONTRACT_TYPE 0 CODE_GENDER 0 FLAG_OWN_CAR 0 FLAG_OWN_REALTY 0 CNT_CHILDREN 0 AMT_INCOME_TOTAL 0 AMT_CREDIT 0 AMT_ANNUITY 12 AMT_GOODS_PRICE 278 NAME_TYPE_SUITE 1292 NAME_INCOME_TYPE 0 NAME_EDUCATION_TYPE 0 NAME_FAMILY_STATUS 0 NAME_HOUSING_TYPE 0 REGION_POPULATION_RELATIVE 0 DAYS_BIRTH 0 DAYS_EMPLOYED 0 DAYS_REGISTRATION 0 DAYS_ID_PUBLISH 0 FLAG_MOBIL 0 FLAG_EMP_PHONE 0 FLAG_WORK_PHONE 0 FLAG_CONT_MOBILE 0 FLAG_PHONE 0 FLAG_EMAIL 0 CNT_FAM_MEMBERS 2 REGION_RATING_CLIENT 0 REGION_RATING_CLIENT_W_CITY 0 WEEKDAY_APPR_PROCESS_START 0 HOUR_APPR_PROCESS_START 0 REG_REGION_NOT_LIVE_REGION 0 REG_REGION_NOT_WORK_REGION 0 LIVE_REGION_NOT_WORK_REGION 0 REG_CITY_NOT_LIVE_CITY 0 REG_CITY_NOT_WORK_CITY 0 LIVE_CITY_NOT_WORK_CITY 0 ORGANIZATION_TYPE 0 OBS_30_CNT_SOCIAL_CIRCLE 1021 DEF_30_CNT_SOCIAL_CIRCLE 1021 OBS_60_CNT_SOCIAL_CIRCLE 1021 DEF_60_CNT_SOCIAL_CIRCLE 1021 DAYS_LAST_PHONE_CHANGE 1 FLAG_DOCUMENT_2 0 FLAG_DOCUMENT_3 0 FLAG_DOCUMENT_4 0 FLAG_DOCUMENT_5 0 FLAG_DOCUMENT_6 0 FLAG_DOCUMENT_7 0 FLAG_DOCUMENT_8 0 FLAG_DOCUMENT_9 0 FLAG_DOCUMENT_10 0 FLAG_DOCUMENT_11 0 FLAG_DOCUMENT_12 0 FLAG_DOCUMENT_13 0 FLAG_DOCUMENT_14 0 FLAG_DOCUMENT_15 0 FLAG_DOCUMENT_16 0 FLAG_DOCUMENT_17 0 FLAG_DOCUMENT_18 0 FLAG_DOCUMENT_19 0 FLAG_DOCUMENT_20 0 FLAG_DOCUMENT_21 0 AMT_REQ_CREDIT_BUREAU_HOUR 41519 AMT_REQ_CREDIT_BUREAU_DAY 41519 AMT_REQ_CREDIT_BUREAU_WEEK 41519 AMT_REQ_CREDIT_BUREAU_MON 41519 AMT_REQ_CREDIT_BUREAU_QRT 41519 AMT_REQ_CREDIT_BUREAU_YEAR 41519 dtype: int64
print("AMT_ANNUITY :" ,application_data['AMT_ANNUITY'].isnull().sum())
AMT_ANNUITY : 12
application_data['AMT_ANNUITY'].describe()
count 307499.000000 mean 27108.573909 std 14493.737315 min 1615.500000 25% 16524.000000 50% 24903.000000 75% 34596.000000 max 258025.500000 Name: AMT_ANNUITY, dtype: float64
sns.set_style('whitegrid')
sns.distplot(application_data['AMT_ANNUITY'])
plt.show()
print("AMT_GOODS_PRICE :" ,application_data['AMT_GOODS_PRICE'].isnull().sum())
AMT_GOODS_PRICE : 278
application_data['AMT_GOODS_PRICE'].describe()
count 3.072330e+05 mean 5.383962e+05 std 3.694465e+05 min 4.050000e+04 25% 2.385000e+05 50% 4.500000e+05 75% 6.795000e+05 max 4.050000e+06 Name: AMT_GOODS_PRICE, dtype: float64
sns.set_style('whitegrid')
sns.distplot(application_data['AMT_GOODS_PRICE'])
plt.show()
print("NAME_TYPE_SUITE :" ,application_data['NAME_TYPE_SUITE'].isnull().sum())
NAME_TYPE_SUITE : 1292
application_data['NAME_TYPE_SUITE'].value_counts()
Unaccompanied 248526 Family 40149 Spouse, partner 11370 Children 3267 Other_B 1770 Other_A 866 Group of people 271 Name: NAME_TYPE_SUITE, dtype: int64
print("CNT_FAM_MEMBERS :" ,application_data['CNT_FAM_MEMBERS'].isnull().sum())
CNT_FAM_MEMBERS : 2
application_data['CNT_FAM_MEMBERS'].describe()
count 307509.000000 mean 2.152665 std 0.910682 min 1.000000 25% 2.000000 50% 2.000000 75% 3.000000 max 20.000000 Name: CNT_FAM_MEMBERS, dtype: float64
sns.set_style('whitegrid')
sns.distplot(application_data['CNT_FAM_MEMBERS'])
plt.show()
print("DAYS_LAST_PHONE_CHANGE :" ,application_data['DAYS_LAST_PHONE_CHANGE'].isnull().sum())
DAYS_LAST_PHONE_CHANGE : 1
application_data['DAYS_LAST_PHONE_CHANGE'].describe()
count 307510.000000 mean -962.858788 std 826.808487 min -4292.000000 25% -1570.000000 50% -757.000000 75% -274.000000 max 0.000000 Name: DAYS_LAST_PHONE_CHANGE, dtype: float64
import statistics
statistics.mode(application_data['DAYS_LAST_PHONE_CHANGE'])
0.0
print(type(application_data.info()))
<class 'pandas.core.frame.DataFrame'> RangeIndex: 307511 entries, 0 to 307510 Data columns (total 70 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 SK_ID_CURR 307511 non-null int64 1 TARGET 307511 non-null int64 2 NAME_CONTRACT_TYPE 307511 non-null object 3 CODE_GENDER 307511 non-null object 4 FLAG_OWN_CAR 307511 non-null object 5 FLAG_OWN_REALTY 307511 non-null object 6 CNT_CHILDREN 307511 non-null int64 7 AMT_INCOME_TOTAL 307511 non-null float64 8 AMT_CREDIT 307511 non-null float64 9 AMT_ANNUITY 307499 non-null float64 10 AMT_GOODS_PRICE 307233 non-null float64 11 NAME_TYPE_SUITE 306219 non-null object 12 NAME_INCOME_TYPE 307511 non-null object 13 NAME_EDUCATION_TYPE 307511 non-null object 14 NAME_FAMILY_STATUS 307511 non-null object 15 NAME_HOUSING_TYPE 307511 non-null object 16 REGION_POPULATION_RELATIVE 307511 non-null float64 17 DAYS_BIRTH 307511 non-null int64 18 DAYS_EMPLOYED 307511 non-null int64 19 DAYS_REGISTRATION 307511 non-null float64 20 DAYS_ID_PUBLISH 307511 non-null int64 21 FLAG_MOBIL 307511 non-null int64 22 FLAG_EMP_PHONE 307511 non-null int64 23 FLAG_WORK_PHONE 307511 non-null int64 24 FLAG_CONT_MOBILE 307511 non-null int64 25 FLAG_PHONE 307511 non-null int64 26 FLAG_EMAIL 307511 non-null int64 27 CNT_FAM_MEMBERS 307509 non-null float64 28 REGION_RATING_CLIENT 307511 non-null int64 29 REGION_RATING_CLIENT_W_CITY 307511 non-null int64 30 WEEKDAY_APPR_PROCESS_START 307511 non-null object 31 HOUR_APPR_PROCESS_START 307511 non-null int64 32 REG_REGION_NOT_LIVE_REGION 307511 non-null int64 33 REG_REGION_NOT_WORK_REGION 307511 non-null int64 34 LIVE_REGION_NOT_WORK_REGION 307511 non-null int64 35 REG_CITY_NOT_LIVE_CITY 307511 non-null int64 36 REG_CITY_NOT_WORK_CITY 307511 non-null int64 37 LIVE_CITY_NOT_WORK_CITY 307511 non-null int64 38 ORGANIZATION_TYPE 307511 non-null object 39 OBS_30_CNT_SOCIAL_CIRCLE 306490 non-null float64 40 DEF_30_CNT_SOCIAL_CIRCLE 306490 non-null float64 41 OBS_60_CNT_SOCIAL_CIRCLE 306490 non-null float64 42 DEF_60_CNT_SOCIAL_CIRCLE 306490 non-null float64 43 DAYS_LAST_PHONE_CHANGE 307510 non-null float64 44 FLAG_DOCUMENT_2 307511 non-null int64 45 FLAG_DOCUMENT_3 307511 non-null int64 46 FLAG_DOCUMENT_4 307511 non-null int64 47 FLAG_DOCUMENT_5 307511 non-null int64 48 FLAG_DOCUMENT_6 307511 non-null int64 49 FLAG_DOCUMENT_7 307511 non-null int64 50 FLAG_DOCUMENT_8 307511 non-null int64 51 FLAG_DOCUMENT_9 307511 non-null int64 52 FLAG_DOCUMENT_10 307511 non-null int64 53 FLAG_DOCUMENT_11 307511 non-null int64 54 FLAG_DOCUMENT_12 307511 non-null int64 55 FLAG_DOCUMENT_13 307511 non-null int64 56 FLAG_DOCUMENT_14 307511 non-null int64 57 FLAG_DOCUMENT_15 307511 non-null int64 58 FLAG_DOCUMENT_16 307511 non-null int64 59 FLAG_DOCUMENT_17 307511 non-null int64 60 FLAG_DOCUMENT_18 307511 non-null int64 61 FLAG_DOCUMENT_19 307511 non-null int64 62 FLAG_DOCUMENT_20 307511 non-null int64 63 FLAG_DOCUMENT_21 307511 non-null int64 64 AMT_REQ_CREDIT_BUREAU_HOUR 265992 non-null float64 65 AMT_REQ_CREDIT_BUREAU_DAY 265992 non-null float64 66 AMT_REQ_CREDIT_BUREAU_WEEK 265992 non-null float64 67 AMT_REQ_CREDIT_BUREAU_MON 265992 non-null float64 68 AMT_REQ_CREDIT_BUREAU_QRT 265992 non-null float64 69 AMT_REQ_CREDIT_BUREAU_YEAR 265992 non-null float64 dtypes: float64(18), int64(41), object(11) memory usage: 164.2+ MB <class 'NoneType'>
application_data['DAYS_BIRTH'] = abs(application_data['DAYS_BIRTH'])
application_data['DAYS_ID_PUBLISH'] = abs(application_data['DAYS_ID_PUBLISH'])
application_data['DAYS_ID_PUBLISH'] = abs(application_data['DAYS_ID_PUBLISH'])
application_data['DAYS_LAST_PHONE_CHANGE'] = abs(application_data['DAYS_LAST_PHONE_CHANGE'])
display("application_data")
display(application_data.head())
'application_data'
| SK_ID_CURR | TARGET | NAME_CONTRACT_TYPE | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | NAME_TYPE_SUITE | NAME_INCOME_TYPE | NAME_EDUCATION_TYPE | NAME_FAMILY_STATUS | NAME_HOUSING_TYPE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | WEEKDAY_APPR_PROCESS_START | HOUR_APPR_PROCESS_START | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | ORGANIZATION_TYPE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 100002 | 1 | Cash loans | M | N | Y | 0 | 202500.0 | 406597.5 | 24700.5 | 351000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.018801 | 9461 | -637 | -3648.0 | 2120 | 1 | 1 | 0 | 1 | 1 | 0 | 1.0 | 2 | 2 | WEDNESDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 2.0 | 2.0 | 2.0 | 2.0 | 1134.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 1 | 100003 | 0 | Cash loans | F | N | N | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2 | 100004 | 0 | Revolving loans | M | Y | Y | 0 | 67500.0 | 135000.0 | 6750.0 | 135000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.010032 | 19046 | -225 | -4260.0 | 2531 | 1 | 1 | 1 | 1 | 1 | 0 | 1.0 | 2 | 2 | MONDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Government | 0.0 | 0.0 | 0.0 | 0.0 | 815.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3 | 100006 | 0 | Cash loans | F | N | Y | 0 | 135000.0 | 312682.5 | 29686.5 | 297000.0 | Unaccompanied | Working | Secondary / secondary special | Civil marriage | House / apartment | 0.008019 | 19005 | -3039 | -9833.0 | 2437 | 1 | 1 | 0 | 1 | 0 | 0 | 2.0 | 2 | 2 | WEDNESDAY | 17 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 2.0 | 0.0 | 2.0 | 0.0 | 617.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 100007 | 0 | Cash loans | M | N | Y | 0 | 121500.0 | 513000.0 | 21865.5 | 513000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.028663 | 19932 | -3038 | -4311.0 | 3458 | 1 | 1 | 0 | 1 | 0 | 0 | 1.0 | 2 | 2 | THURSDAY | 11 | 0 | 0 | 0 | 0 | 1 | 1 | Religion | 0.0 | 0.0 | 0.0 | 0.0 | 1106.0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
obj_dtypes = [i for i in application_data.select_dtypes(include=object).columns if i not in ["type"] ]
num_dtypes = [i for i in application_data.select_dtypes(include = np.number).columns if i not in ['SK_ID_CURR'] + [ 'TARGET']]
print(color.BOLD + color.PURPLE + 'Categorical Columns' + color.END, "\n")
for x in range(len(obj_dtypes)):
print(obj_dtypes[x])
Categorical Columns
NAME_CONTRACT_TYPE
CODE_GENDER
FLAG_OWN_CAR
FLAG_OWN_REALTY
NAME_TYPE_SUITE
NAME_INCOME_TYPE
NAME_EDUCATION_TYPE
NAME_FAMILY_STATUS
NAME_HOUSING_TYPE
WEEKDAY_APPR_PROCESS_START
ORGANIZATION_TYPE
print(color.BOLD + color.PURPLE +"Numerical Columns" + color.END, "\n")
for x in range(len(num_dtypes)):
print(num_dtypes[x])
Numerical Columns
CNT_CHILDREN
AMT_INCOME_TOTAL
AMT_CREDIT
AMT_ANNUITY
AMT_GOODS_PRICE
REGION_POPULATION_RELATIVE
DAYS_BIRTH
DAYS_EMPLOYED
DAYS_REGISTRATION
DAYS_ID_PUBLISH
FLAG_MOBIL
FLAG_EMP_PHONE
FLAG_WORK_PHONE
FLAG_CONT_MOBILE
FLAG_PHONE
FLAG_EMAIL
CNT_FAM_MEMBERS
REGION_RATING_CLIENT
REGION_RATING_CLIENT_W_CITY
HOUR_APPR_PROCESS_START
REG_REGION_NOT_LIVE_REGION
REG_REGION_NOT_WORK_REGION
LIVE_REGION_NOT_WORK_REGION
REG_CITY_NOT_LIVE_CITY
REG_CITY_NOT_WORK_CITY
LIVE_CITY_NOT_WORK_CITY
OBS_30_CNT_SOCIAL_CIRCLE
DEF_30_CNT_SOCIAL_CIRCLE
OBS_60_CNT_SOCIAL_CIRCLE
DEF_60_CNT_SOCIAL_CIRCLE
DAYS_LAST_PHONE_CHANGE
FLAG_DOCUMENT_2
FLAG_DOCUMENT_3
FLAG_DOCUMENT_4
FLAG_DOCUMENT_5
FLAG_DOCUMENT_6
FLAG_DOCUMENT_7
FLAG_DOCUMENT_8
FLAG_DOCUMENT_9
FLAG_DOCUMENT_10
FLAG_DOCUMENT_11
FLAG_DOCUMENT_12
FLAG_DOCUMENT_13
FLAG_DOCUMENT_14
FLAG_DOCUMENT_15
FLAG_DOCUMENT_16
FLAG_DOCUMENT_17
FLAG_DOCUMENT_18
FLAG_DOCUMENT_19
FLAG_DOCUMENT_20
FLAG_DOCUMENT_21
AMT_REQ_CREDIT_BUREAU_HOUR
AMT_REQ_CREDIT_BUREAU_DAY
AMT_REQ_CREDIT_BUREAU_WEEK
AMT_REQ_CREDIT_BUREAU_MON
AMT_REQ_CREDIT_BUREAU_QRT
AMT_REQ_CREDIT_BUREAU_YEAR
fig = plt.figure(figsize=(13,6))
plt.subplot(121)
application_data["CODE_GENDER"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["red","yellow"],startangle = 60,
wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[.05,0,0],shadow =True)
plt.title("Distribution of gender")
plt.show()
plt.figure(figsize=(14,7))
plt.subplot(121)
application_data["TARGET"].value_counts().plot.pie(autopct = "%1.0f%%",colors = sns.color_palette("prism",7),startangle = 60,labels=["repayer","defaulter"],
wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[.1,0],shadow =True)
plt.title("Distribution of target variable")
plt.subplot(122)
ax = application_data["TARGET"].value_counts().plot(kind="barh")
for i,j in enumerate(application_data["TARGET"].value_counts().values):
ax.text(.7,i,j,weight = "bold",fontsize=20)
plt.title("Count of target variable")
plt.show()
application_data_x = application_data[[x for x in application_data.columns if x not in ["TARGET"]]]
previous_application_x = previous_application[[x for x in previous_application.columns if x not in ["TARGET"]]]
application_data_x["type"] = "application_data"
previous_application_x["type"] = "previous_application"
data = pd.concat([application_data_x,previous_application_x],axis=0)
plt.figure(figsize=(14,7))
plt.subplot(121)
data[data["type"] == "application_data"]["NAME_CONTRACT_TYPE"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["orange","red"],startangle = 60,
wedgeprops={"linewidth":2,"edgecolor":"white"},shadow =True)
circ = plt.Circle((0,0),.7,color="white")
plt.gca().add_artist(circ)
plt.title("distribution of contract types in application_data")
plt.subplot(122)
data[data["type"] == "previous_application"]["NAME_CONTRACT_TYPE"].value_counts().plot.pie(autopct = "%1.2f%%",colors = ["red","yellow","green",'BLACK'],startangle = 60,
wedgeprops={"linewidth":2,"edgecolor":"white"},shadow =True)
circ = plt.Circle((0,0),.7,color="white")
plt.gca().add_artist(circ)
plt.ylabel("")
plt.title("distribution of contract types in previous_application")
plt.show()
plt.show()
fig = plt.figure(figsize=(13,6))
plt.subplot(121)
data[data["type"] == "application_data"]["CODE_GENDER"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["red","yellow"],startangle = 60,
wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[.05,0,0],shadow =True)
plt.title("distribution of gender in application_data")
plt.show()
fig = plt.figure(figsize=(13,6))
plt.subplot(121)
ax = sns.countplot("NAME_CONTRACT_TYPE",hue="CODE_GENDER",data=data[data["type"] == "application_data"],palette=["r","b","g"])
ax.set_facecolor("lightgrey")
ax.set_title("Distribution of Contract type by gender -application_data")
plt.show()
fig = plt.figure(figsize=(13,6))
plt.subplot(121)
data["FLAG_OWN_CAR"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["gold","orangered"],startangle = 60,
wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[.05,0],shadow =True)
plt.title("distribution of client owning a car")
plt.subplot(122)
data[data["FLAG_OWN_CAR"] == "Y"]["CODE_GENDER"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["b","orangered"],startangle = 90,
wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[.05,0,0],shadow =True)
plt.title("distribution of client owning a car by gender")
plt.show()
plt.figure(figsize=(13,6))
plt.subplot(121)
data["FLAG_OWN_REALTY"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["skyblue","gold"],startangle = 90,
wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[0.05,0],shadow =True)
plt.title("Distribution of client owns a house or flat")
plt.subplot(122)
data[data["FLAG_OWN_REALTY"] == "Y"]["CODE_GENDER"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["orangered","b"],startangle = 90,
wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[.05,0,0],shadow =True)
plt.title("Distribution of client owning a house or flat by gender")
plt.show()
fig = plt.figure(figsize=(12,10))
plt.subplot(211)
sns.countplot(application_data["CNT_CHILDREN"],palette="Set1",hue=application_data["TARGET"])
plt.legend(loc="upper center")
plt.title(" Distribution of Number of children client has by repayment status")
plt.subplot(212)
sns.countplot(application_data["CNT_FAM_MEMBERS"],palette="Set1",hue=application_data["TARGET"])
plt.legend(loc="upper center")
plt.title(" Distribution of Number of family members client has by repayment status")
fig.set_facecolor("lightblue")
default = application_data[application_data["TARGET"]==1][[ 'NAME_CONTRACT_TYPE', 'CODE_GENDER','FLAG_OWN_CAR', 'FLAG_OWN_REALTY']]
non_default = application_data[application_data["TARGET"]==0][[ 'NAME_CONTRACT_TYPE', 'CODE_GENDER','FLAG_OWN_CAR', 'FLAG_OWN_REALTY']]
d_cols = ['NAME_CONTRACT_TYPE', 'CODE_GENDER','FLAG_OWN_CAR', 'FLAG_OWN_REALTY']
d_length = len(d_cols)
fig = plt.figure(figsize=(16,4))
for i,j in itertools.zip_longest(d_cols,range(d_length)):
plt.subplot(1,4,j+1)
default[i].value_counts().plot.pie(autopct = "%1.0f%%",colors = sns.color_palette("prism"),startangle = 90,
wedgeprops={"linewidth":1,"edgecolor":"white"},shadow =True)
circ = plt.Circle((0,0),.7,color="white")
plt.gca().add_artist(circ)
plt.ylabel("")
plt.title(i+"-Defaulter")
fig = plt.figure(figsize=(16,4))
for i,j in itertools.zip_longest(d_cols,range(d_length)):
plt.subplot(1,4,j+1)
non_default[i].value_counts().plot.pie(autopct = "%1.0f%%",colors = sns.color_palette("prism",3),startangle = 90,
wedgeprops={"linewidth":1,"edgecolor":"white"},shadow =True)
circ = plt.Circle((0,0),.7,color="white")
plt.gca().add_artist(circ)
plt.ylabel("")
plt.title(i+"-Repayer")
cols = [ 'AMT_INCOME_TOTAL', 'AMT_CREDIT','AMT_ANNUITY', 'AMT_GOODS_PRICE']
length = len(cols)
cs = ["r","b","g","k"]
ax = plt.figure(figsize=(18,18))
ax.set_facecolor("lightgrey")
for i,j,k in itertools.zip_longest(cols,range(length),cs):
plt.subplot(2,2,j+1)
sns.distplot(data[data[i].notnull()][i],color=k)
plt.axvline(data[i].mean(),label = "mean",linestyle="dashed",color="k")
plt.legend(loc="best")
plt.title(i)
plt.subplots_adjust(hspace = .2)
df = application_data.groupby("TARGET")[cols].describe().transpose().reset_index()
df = df[df["level_1"].isin([ 'mean', 'std', 'min', 'max'])]
df_x = df[["level_0","level_1",0]]
df_y = df[["level_0","level_1",1]]
df_x = df_x.rename(columns={'level_0':"amount_type", 'level_1':"statistic", 0:"amount"})
df_x["type"] = "REPAYER"
df_y = df_y.rename(columns={'level_0':"amount_type", 'level_1':"statistic", 1:"amount"})
df_y["type"] = "DEFAULTER"
df_new = pd.concat([df_x,df_y],axis = 0)
stat = df_new["statistic"].unique().tolist()
length = len(stat)
plt.figure(figsize=(13,15))
for i,j in itertools.zip_longest(stat,range(length)):
plt.subplot(2,2,j+1)
fig = sns.barplot(df_new[df_new["statistic"] == i]["amount_type"],df_new[df_new["statistic"] == i]["amount"],
hue=df_new[df_new["statistic"] == i]["type"],palette=["g","r"])
plt.title(i + "--Defaulters vs Non defaulters")
plt.subplots_adjust(hspace = .4)
fig.set_facecolor("lightgrey")
cols = [ 'AMT_INCOME_TOTAL', 'AMT_CREDIT','AMT_ANNUITY', 'AMT_GOODS_PRICE']
df1 = data.groupby("CODE_GENDER")[cols].mean().transpose().reset_index()
df_f = df1[["index","F"]]
df_f = df_f.rename(columns={'index':"amt_type", 'F':"amount"})
df_f["gender"] = "FEMALE"
df_m = df1[["index","M"]]
df_m = df_m.rename(columns={'index':"amt_type", 'M':"amount"})
df_m["gender"] = "MALE"
df_xna = df1[["index","XNA"]]
df_xna = df_xna.rename(columns={'index':"amt_type", 'XNA':"amount"})
df_xna["gender"] = "XNA"
df_gen = pd.concat([df_m,df_f,df_xna],axis=0)
plt.figure(figsize=(12,5))
ax = sns.barplot("amt_type","amount",data=df_gen,hue="gender",palette="Set1")
plt.title("Average Income,credit,annuity & goods_price by gender")
plt.show()
fig = plt.figure(figsize=(10,8))
plt.scatter(application_data[application_data["TARGET"]==0]['AMT_ANNUITY'],application_data[application_data["TARGET"]==0]['AMT_CREDIT'],s=35,
color="b",alpha=.5,label="REPAYER",linewidth=.5,edgecolor="k")
plt.scatter(application_data[application_data["TARGET"]==1]['AMT_ANNUITY'],application_data[application_data["TARGET"]==1]['AMT_CREDIT'],s=35,
color="r",alpha=.2,label="DEFAULTER",linewidth=.5,edgecolor="k")
plt.legend(loc="best",prop={"size":15})
plt.xlabel("AMT_ANNUITY")
plt.ylabel("AMT_CREDIT")
plt.title("Scatter plot between credit amount and annuity amount")
plt.show()
amt = application_data[[ 'AMT_INCOME_TOTAL','AMT_CREDIT',
'AMT_ANNUITY', 'AMT_GOODS_PRICE',"TARGET"]]
amt = amt[(amt["AMT_GOODS_PRICE"].notnull()) & (amt["AMT_ANNUITY"].notnull())]
sns.pairplot(amt,hue="TARGET",palette=["b","r"])
plt.show()
plt.figure(figsize=(18,12))
plt.subplot(121)
sns.countplot(y=data["NAME_TYPE_SUITE"],
palette="Set2",
order=data["NAME_TYPE_SUITE"].value_counts().index[:5])
plt.title("Distribution of Suite type")
plt.subplot(122)
sns.countplot(y=data["NAME_TYPE_SUITE"],
hue=data["CODE_GENDER"],palette="Set2",
order=data["NAME_TYPE_SUITE"].value_counts().index[:5])
plt.ylabel("")
plt.title("Distribution of Suite type by gender")
plt.subplots_adjust(wspace = .4)
plt.figure(figsize=(18,12))
plt.subplot(121)
sns.countplot(y=data["NAME_INCOME_TYPE"],
palette="Set2",
order=data["NAME_INCOME_TYPE"].value_counts().index[:4])
plt.title("Distribution of client income type")
plt.subplot(122)
sns.countplot(y=data["NAME_INCOME_TYPE"],
hue=data["CODE_GENDER"],
palette="Set2",
order=data["NAME_INCOME_TYPE"].value_counts().index[:4])
plt.ylabel("")
plt.title("Distribution of client income type by gender")
plt.subplots_adjust(wspace = .4)
plt.figure(figsize=(25,25))
plt.subplot(121)
application_data[application_data["TARGET"]==0]["NAME_EDUCATION_TYPE"].value_counts().plot.pie(fontsize=12,autopct = "%1.0f%%",
colors = sns.color_palette("Set1"),
wedgeprops={"linewidth":2,"edgecolor":"white"},shadow =True)
circ = plt.Circle((0,0),.7,color="white")
plt.gca().add_artist(circ)
plt.title("Distribution of Education type for Repayers",color="b")
plt.subplot(122)
application_data[application_data["TARGET"]==1]["NAME_EDUCATION_TYPE"].value_counts().plot.pie(fontsize=12,autopct = "%1.0f%%",
colors = sns.color_palette("Set1"),
wedgeprops={"linewidth":2,"edgecolor":"white"},shadow =True)
circ = plt.Circle((0,0),.7,color="white")
plt.gca().add_artist(circ)
plt.title("Distribution of Education type for Defaulters",color="b")
plt.ylabel("")
plt.show()
edu = data.groupby(['NAME_EDUCATION_TYPE','NAME_INCOME_TYPE'])['AMT_INCOME_TOTAL'].mean().reset_index().sort_values(by='AMT_INCOME_TOTAL',ascending=False)
fig = plt.figure(figsize=(13,7))
ax = sns.barplot('NAME_INCOME_TYPE','AMT_INCOME_TOTAL',data=edu,hue='NAME_EDUCATION_TYPE',palette="seismic")
ax.set_facecolor("k")
plt.title(" Average Earnings by different professions and education types")
plt.show()
plt.figure(figsize=(16,8))
plt.subplot(121)
application_data[application_data["TARGET"]==0]["NAME_FAMILY_STATUS"].value_counts().plot.pie(autopct = "%1.0f%%",
startangle=120,colors = sns.color_palette("Set2",7),
wedgeprops={"linewidth":2,"edgecolor":"white"},shadow =True,explode=[0,.07,0,0,0,0])
plt.title("Distribution of Family status for Repayers",color="b")
plt.subplot(122)
application_data[application_data["TARGET"]==1]["NAME_FAMILY_STATUS"].value_counts().plot.pie(autopct = "%1.0f%%",
startangle=120,colors = sns.color_palette("Set2",7),
wedgeprops={"linewidth":2,"edgecolor":"white"},shadow =True,explode=[0,.07,0,0,0])
plt.title("Distribution of Family status for Defaulters",color="b")
plt.ylabel("")
plt.show()
plt.figure(figsize=(20,20))
plt.subplot(121)
application_data[application_data["TARGET"]==0]["NAME_HOUSING_TYPE"].value_counts().plot.pie(autopct = "%1.0f%%",fontsize=10,
colors = sns.color_palette("Spectral"),
wedgeprops={"linewidth":2,"edgecolor":"white"},shadow =True)
plt.title("Distribution of housing type for Repayer",color="b")
plt.subplot(122)
application_data[application_data["TARGET"]==1]["NAME_HOUSING_TYPE"].value_counts().plot.pie(autopct = "%1.0f%%",fontsize=10,
colors = sns.color_palette("Spectral"),
wedgeprops={"linewidth":2,"edgecolor":"white"},shadow =True)
plt.title("Distribution of housing type for Defaulters",color="b")
plt.ylabel("")
plt.show()
fig = plt.figure(figsize=(13,8))
plt.subplot(121)
sns.violinplot(y=application_data[application_data["TARGET"]==0]["REGION_POPULATION_RELATIVE"]
,x=application_data[application_data["TARGET"]==0]["NAME_CONTRACT_TYPE"],
palette="Set1")
plt.title("Distribution of region population for Non Default loans",color="b")
plt.subplot(122)
sns.violinplot(y = application_data[application_data["TARGET"]==1]["REGION_POPULATION_RELATIVE"]
,x=application_data[application_data["TARGET"]==1]["NAME_CONTRACT_TYPE"]
,palette="Set1")
plt.title("Distribution of region population for Default loans",color="b")
plt.subplots_adjust(wspace = .2)
fig.set_facecolor("lightgrey")
import matplotlib.pyplot as plt
import seaborn as sns
fig = plt.figure(figsize=(13, 15))
plt.subplot(221)
sns.distplot(application_data[application_data["TARGET"] == 0]["DAYS_BIRTH"], color="b")
plt.title("Age Distribution of repayers")
plt.subplot(222)
sns.distplot(application_data[application_data["TARGET"] == 1]["DAYS_BIRTH"], color="r")
plt.title("Age Distribution of defaulters")
plt.subplot(223)
sns.boxplot(x="TARGET", y="DAYS_BIRTH", hue="CODE_GENDER", data=application_data, palette=["b", "grey", "m"])
plt.axhline(application_data["DAYS_BIRTH"].mean(), linestyle="dashed", color="k", label="average age of client")
plt.legend(loc="lower right")
plt.title("Client age vs Loan repayment status (hue=gender)")
plt.subplot(224)
sns.boxplot(x="TARGET", y="DAYS_BIRTH", hue="NAME_CONTRACT_TYPE", data=application_data, palette=["r", "g"])
plt.axhline(application_data["DAYS_BIRTH"].mean(), linestyle="dashed", color="k", label="average age of client")
plt.legend(loc="lower right")
plt.title("Client age vs Loan repayment status (hue=contract type)")
plt.subplots_adjust(wspace=.2, hspace=.3)
fig.set_facecolor("lightgrey")
plt.show()
fig = plt.figure(figsize=(13,5))
plt.subplot(121)
sns.distplot(application_data[application_data["TARGET"]==0]["DAYS_EMPLOYED"],color="b")
plt.title("days employed distribution of repayers")
plt.subplot(122)
sns.distplot(application_data[application_data["TARGET"]==1]["DAYS_EMPLOYED"],color="r")
plt.title("days employed distribution of defaulters")
fig.set_facecolor("ghostwhite")
fig = plt.figure(figsize=(13,5))
plt.subplot(121)
sns.distplot(application_data[application_data["TARGET"]==0]["DAYS_REGISTRATION"],color="b")
plt.title("registration days distribution of repayers")
plt.subplot(122)
sns.distplot(application_data[application_data["TARGET"]==1]["DAYS_REGISTRATION"],color="r")
plt.title("registration days distribution of defaulter")
fig.set_facecolor("ghostwhite")
x = application_data[['FLAG_MOBIL', 'FLAG_EMP_PHONE', 'FLAG_WORK_PHONE', 'FLAG_CONT_MOBILE',
'FLAG_PHONE', 'FLAG_EMAIL',"TARGET"]]
x["TARGET"] = x["TARGET"].replace({0:"repayers",1:"defaulters"})
x = x.replace({1:"YES",0:"NO"})
cols = ['FLAG_MOBIL', 'FLAG_EMP_PHONE', 'FLAG_WORK_PHONE', 'FLAG_CONT_MOBILE',
'FLAG_PHONE', 'FLAG_EMAIL']
length = len(cols)
fig = plt.figure(figsize=(15,12))
fig.set_facecolor("lightgrey")
for i,j in itertools.zip_longest(cols,range(length)):
plt.subplot(2,3,j+1)
sns.countplot(x[i],hue=x["TARGET"],palette=["r","g"])
plt.title(i,color="b")
fig = plt.figure(figsize=(13,13))
plt.subplot(221)
application_data[application_data["TARGET"]==0]["REGION_RATING_CLIENT"].value_counts().plot.pie(autopct = "%1.0f%%",fontsize=12,
colors = sns.color_palette("Pastel1"),
wedgeprops={"linewidth":2,"edgecolor":"white"},shadow =True)
plt.title("Distribution of region rating for Repayers",color="b")
plt.subplot(222)
application_data[application_data["TARGET"]==1]["REGION_RATING_CLIENT"].value_counts().plot.pie(autopct = "%1.0f%%",fontsize=12,
colors = sns.color_palette("Pastel1"),
wedgeprops={"linewidth":2,"edgecolor":"white"},shadow =True)
plt.title("Distribution of region rating for Defaulters",color="b")
plt.ylabel("")
plt.subplot(223)
application_data[application_data["TARGET"]==0]["REGION_RATING_CLIENT_W_CITY"].value_counts().plot.pie(autopct = "%1.0f%%",fontsize=12,
colors = sns.color_palette("Paired"),
wedgeprops={"linewidth":2,"edgecolor":"white"},shadow =True)
plt.title("Distribution of city region rating for Repayers",color="b")
plt.subplot(224)
application_data[application_data["TARGET"]==1]["REGION_RATING_CLIENT_W_CITY"].value_counts().plot.pie(autopct = "%1.0f%%",fontsize=12,
colors = sns.color_palette("Paired"),
wedgeprops={"linewidth":2,"edgecolor":"white"},shadow =True)
plt.title("Distribution of city region rating for Defaulters",color="b")
plt.ylabel("")
fig.set_facecolor("ivory")
day = application_data.groupby("TARGET").agg({"WEEKDAY_APPR_PROCESS_START": "value_counts"})
day = day.rename(columns={"WEEKDAY_APPR_PROCESS_START": "value_counts"}).reset_index()
day_0 = day[:7]
day_1 = day[7:]
day_0["percentage"] = day_0["value_counts"] * 100 / day_0["value_counts"].sum()
day_1["percentage"] = day_1["value_counts"] * 100 / day_1["value_counts"].sum()
days = pd.concat([day_0, day_1], axis=0)
# Create a new column based on the "TARGET" values
days["TARGET"] = days["TARGET"].map({1: "defaulters", 0: "repayers"})
fig = plt.figure(figsize=(13, 15))
plt.subplot(211)
order = ['SUNDAY', 'MONDAY', 'TUESDAY', 'WEDNESDAY', 'THURSDAY', 'FRIDAY', 'SATURDAY']
ax = sns.barplot("WEEKDAY_APPR_PROCESS_START", "percentage", data=days,
hue="TARGET", order=order, palette="prism")
ax.set_facecolor("k")
ax.set_title("Peak days for applying loans (defaulters vs repayers)")
hr = application_data.groupby("TARGET").agg({"HOUR_APPR_PROCESS_START": "value_counts"})
hr = hr.rename(columns={"HOUR_APPR_PROCESS_START": "value_counts"}).reset_index()
hr_0 = hr[hr["TARGET"] == 0]
hr_1 = hr[hr["TARGET"] == 1]
hr_0["percentage"] = hr_0["value_counts"] * 100 / hr_0["value_counts"].sum()
hr_1["percentage"] = hr_1["value_counts"] * 100 / hr_1["value_counts"].sum()
hrs = pd.concat([hr_0, hr_1], axis=0)
# Create a new column based on the "TARGET" values
hrs["TARGET"] = hrs["TARGET"].map({1: "defaulters", 0: "repayers"})
hrs = hrs.sort_values(by="HOUR_APPR_PROCESS_START", ascending=True)
plt.subplot(212)
ax1 = sns.pointplot("HOUR_APPR_PROCESS_START", "percentage", data=hrs, hue="TARGET", palette="prism")
ax1.set_facecolor("k")
ax1.set_title("Peak hours for applying loans (defaulters vs repayers")
fig.set_facecolor("snow")
org = application_data.groupby("TARGET").agg({"ORGANIZATION_TYPE":"value_counts"})
org = org.rename(columns = {"ORGANIZATION_TYPE":"value_counts"}).reset_index()
org_0 = org[org["TARGET"] == 0]
org_1 = org[org["TARGET"] == 1]
org_0["percentage"] = org_0["value_counts"]*100/org_0["value_counts"].sum()
org_1["percentage"] = org_1["value_counts"]*100/org_1["value_counts"].sum()
organization = pd.concat([org_0,org_1],axis=0)
organization = organization.sort_values(by="ORGANIZATION_TYPE",ascending=True)
organization["TARGET"] = organization["TARGET"].replace({0:"repayers",1:"defaulters"})
organization
plt.figure(figsize=(13,7))
ax = sns.pointplot("ORGANIZATION_TYPE","percentage",
data=organization,hue="TARGET",palette=["b","r"])
plt.xticks(rotation=90)
plt.grid(True,alpha=.3)
ax.set_facecolor("k")
ax.set_title("Distribution in organization types for repayers and defaulters")
plt.show()
fig = plt.figure(figsize=(20,20))
plt.subplot(421)
sns.boxplot(data=application_data,x='TARGET',y='OBS_30_CNT_SOCIAL_CIRCLE',
hue="TARGET", palette="Set3")
plt.title("Client's social surroundings with observable 30 DPD (days past due) def",color="b")
plt.subplot(422)
sns.boxplot(data=application_data,x='TARGET',y='DEF_30_CNT_SOCIAL_CIRCLE',
hue="TARGET", palette="Set3")
plt.title("Client's social surroundings defaulted on 30 DPD (days past due)",color="b")
plt.subplot(423)
sns.boxplot(data=application_data,x='TARGET',y='OBS_60_CNT_SOCIAL_CIRCLE',
hue="TARGET", palette="Set3")
plt.title("Client's social surroundings with observable 60 DPD (days past due) default",color="b")
plt.subplot(424)
sns.boxplot(data=application_data,x='TARGET',y='DEF_60_CNT_SOCIAL_CIRCLE',
hue="TARGET", palette="Set3")
plt.title("Client's social surroundings defaulted on 60 DPD (days past due)",color="b")
fig.set_facecolor("ghostwhite")
plt.figure(figsize=(13, 7))
plt.subplot(121)
ax = sns.violinplot(application_data["TARGET"],
application_data["DAYS_LAST_PHONE_CHANGE"], palette=["g", "r"])
ax.set_facecolor("oldlace")
ax.set_title("Days before application client changed phone - Violin Plot")
plt.subplot(122)
ax1 = sns.boxplot(x=application_data["TARGET"],
y=application_data["DAYS_LAST_PHONE_CHANGE"], palette=["g", "r"])
ax1.set_facecolor("oldlace")
ax1.set_ylabel("")
ax1.set_title("Days before application client changed phone - Box Plot")
plt.subplots_adjust(wspace=0.2)
cols = [ 'FLAG_DOCUMENT_2', 'FLAG_DOCUMENT_3',
'FLAG_DOCUMENT_4', 'FLAG_DOCUMENT_5', 'FLAG_DOCUMENT_6',
'FLAG_DOCUMENT_7', 'FLAG_DOCUMENT_8', 'FLAG_DOCUMENT_9',
'FLAG_DOCUMENT_10', 'FLAG_DOCUMENT_11', 'FLAG_DOCUMENT_12',
'FLAG_DOCUMENT_13', 'FLAG_DOCUMENT_14', 'FLAG_DOCUMENT_15',
'FLAG_DOCUMENT_16', 'FLAG_DOCUMENT_17', 'FLAG_DOCUMENT_18',
'FLAG_DOCUMENT_19', 'FLAG_DOCUMENT_20', 'FLAG_DOCUMENT_21']
df_flag = application_data[cols+["TARGET"]]
length = len(cols)
df_flag["TARGET"] = df_flag["TARGET"].replace({1:"defaulter",0:"repayer"})
fig = plt.figure(figsize=(13,24))
fig.set_facecolor("lightgrey")
for i,j in itertools.zip_longest(cols,range(length)):
plt.subplot(5,4,j+1)
ax = sns.countplot(df_flag[i],hue=df_flag["TARGET"],palette=["r","b"])
plt.yticks(fontsize=5)
plt.xlabel("")
plt.title(i)
ax.set_facecolor("k")
cols = ['AMT_REQ_CREDIT_BUREAU_HOUR', 'AMT_REQ_CREDIT_BUREAU_DAY',
'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_MON',
'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR']
application_data.groupby("TARGET")[cols].max().transpose().plot(kind="barh",
figsize=(10,5),width=.8)
plt.title("Maximum enquries made by defaulters and repayers")
application_data.groupby("TARGET")[cols].mean().transpose().plot(kind="barh",
figsize=(10,5),width=.8)
plt.title("average enquries made by defaulters and repayers")
application_data.groupby("TARGET")[cols].std().transpose().plot(kind="barh",
figsize=(10,5),width=.8)
plt.title("standard deviation in enquries made by defaulters and repayers")
plt.show()
x = previous_application.groupby("SK_ID_CURR")["SK_ID_PREV"].count().reset_index()
plt.figure(figsize=(13,7))
ax = sns.distplot(x["SK_ID_PREV"],color="orange")
plt.axvline(x["SK_ID_PREV"].mean(),linestyle="dashed",color="r",label="average")
plt.axvline(x["SK_ID_PREV"].std(),linestyle="dashed",color="b",label="standard deviation")
plt.axvline(x["SK_ID_PREV"].max(),linestyle="dashed",color="g",label="maximum")
plt.legend(loc="best")
plt.title("Current loan id having previous loan applications")
ax.set_facecolor("k")
pip install squarify
Defaulting to user installation because normal site-packages is not writeable Collecting squarify Downloading squarify-0.4.3-py3-none-any.whl (4.3 kB) Installing collected packages: squarify Successfully installed squarify-0.4.3 Note: you may need to restart the kernel to use updated packages.
cnts = previous_application["NAME_CONTRACT_TYPE"].value_counts()
import squarify
plt.figure(figsize=(8,6))
squarify.plot(cnts.values,label=cnts.keys(),value=cnts.values,linewidth=2,edgecolor="k",alpha=.8,color=sns.color_palette("Set1"))
plt.axis("off")
plt.title("Contaract types in previous applications")
plt.show()
--------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) ~\AppData\Local\Temp\ipykernel_13572\2131491753.py in <module> 1 cnts = previous_application["NAME_CONTRACT_TYPE"].value_counts() ----> 2 import squarify 3 plt.figure(figsize=(8,6)) 4 squarify.plot(cnts.values,label=cnts.keys(),value=cnts.values,linewidth=2,edgecolor="k",alpha=.8,color=sns.color_palette("Set1")) 5 plt.axis("off") ModuleNotFoundError: No module named 'squarify'
plt.figure(figsize=(20,20))
plt.subplot(211)
ax = sns.kdeplot(previous_application["AMT_APPLICATION"],color="b",linewidth=3)
ax = sns.kdeplot(previous_application[previous_application["AMT_CREDIT"].notnull()]["AMT_CREDIT"],color="r",linewidth=3)
plt.axvline(previous_application[previous_application["AMT_CREDIT"].notnull()]["AMT_CREDIT"].mean(),color="r",linestyle="dashed",label="AMT_APPLICATION_MEAN")
plt.axvline(previous_application["AMT_APPLICATION"].mean(),color="b",linestyle="dashed",label="AMT_APPLICATION_MEAN")
plt.legend(loc="best")
plt.title("Previous loan amounts applied and loan amounts credited.")
ax.set_facecolor("k")
plt.subplot(212)
diff = (previous_application["AMT_CREDIT"] - previous_application["AMT_APPLICATION"]).reset_index()
diff = diff[diff[0].notnull()]
ax1 = sns.kdeplot(diff[0],color="g",linewidth=3,label = "difference in amount requested by client and amount credited")
plt.axvline(diff[0].mean(),color="white",linestyle="dashed",label = "mean")
plt.title("difference in amount requested by client and amount credited")
ax1.legend(loc="best")
ax1.set_facecolor("k")
mn = previous_application.groupby("NAME_CONTRACT_TYPE")[["AMT_APPLICATION","AMT_CREDIT"]].mean().stack().reset_index()
tt = previous_application.groupby("NAME_CONTRACT_TYPE")[["AMT_APPLICATION","AMT_CREDIT"]].sum().stack().reset_index()
fig = plt.figure(figsize=(10,13))
fig.set_facecolor("ghostwhite")
plt.subplot(211)
ax = sns.barplot(0,"NAME_CONTRACT_TYPE",data=mn[:6],hue="level_1",palette="inferno")
ax.set_facecolor("k")
ax.set_xlabel("average amounts")
ax.set_title("Average amounts by contract types")
plt.subplot(212)
ax1 = sns.barplot(0,"NAME_CONTRACT_TYPE",data=tt[:6],hue="level_1",palette="magma")
ax1.set_facecolor("k")
ax1.set_xlabel("total amounts")
ax1.set_title("total amounts by contract types")
plt.subplots_adjust(hspace = .2)
plt.show()
plt.figure(figsize=(14,5))
plt.subplot(121)
previous_application.groupby("NAME_CONTRACT_TYPE")["AMT_ANNUITY"].sum().plot(kind="bar")
plt.xticks(rotation=0)
plt.title("Total annuity amount by contract types in previous applications")
plt.subplot(122)
previous_application.groupby("NAME_CONTRACT_TYPE")["AMT_ANNUITY"].mean().plot(kind="bar")
plt.title("average annuity amount by contract types in previous applications")
plt.xticks(rotation=0)
plt.show()
ax = pd.crosstab(previous_application["NAME_CONTRACT_TYPE"],previous_application["NAME_CONTRACT_STATUS"]).plot(kind="barh",figsize=(10,7),stacked=True)
plt.xticks(rotation =0)
plt.ylabel("count")
plt.title("Count of application status by application type")
ax.set_facecolor("k")
hr = pd.crosstab(previous_application["WEEKDAY_APPR_PROCESS_START"],previous_application["NAME_CONTRACT_STATUS"]).stack().reset_index()
plt.figure(figsize=(12,8))
ax = sns.pointplot(hr["WEEKDAY_APPR_PROCESS_START"],hr[0],hue=hr["NAME_CONTRACT_STATUS"],palette=["g","r","b","orange"],scale=1)
ax.set_facecolor("k")
ax.set_ylabel("count")
ax.set_title("Contract status by weekdays")
plt.grid(True,alpha=.2)
hr = pd.crosstab(previous_application["HOUR_APPR_PROCESS_START"],previous_application["NAME_CONTRACT_STATUS"]).stack().reset_index()
plt.figure(figsize=(12,8))
ax = sns.pointplot(hr["HOUR_APPR_PROCESS_START"],hr[0],hue=hr["NAME_CONTRACT_STATUS"],palette=["g","r","b","orange"],scale=1)
ax.set_facecolor("k")
ax.set_ylabel("count")
ax.set_title("Contract status by day hours.")
plt.grid(True,alpha=.2)
hr = pd.crosstab(previous_application["HOUR_APPR_PROCESS_START"],previous_application["WEEKDAY_APPR_PROCESS_START"]).stack().reset_index()
plt.figure(figsize=(12,8))
ax = sns.pointplot(hr["HOUR_APPR_PROCESS_START"],hr[0],hue=hr["WEEKDAY_APPR_PROCESS_START"],palette=["g","r","b","orange"],scale=1)
ax.set_facecolor("k")
ax.set_ylabel("count")
ax.set_title("Peak hours for week days")
plt.grid(True,alpha=.2)
previous_application[["NAME_CASH_LOAN_PURPOSE","NAME_CONTRACT_STATUS"]]
purpose = pd.crosstab(previous_application["NAME_CASH_LOAN_PURPOSE"],previous_application["NAME_CONTRACT_STATUS"])
purpose["a"] = (purpose["Approved"]*100)/(purpose["Approved"]+purpose["Canceled"]+purpose["Refused"]+purpose["Unused offer"])
purpose["c"] = (purpose["Canceled"]*100)/(purpose["Approved"]+purpose["Canceled"]+purpose["Refused"]+purpose["Unused offer"])
purpose["r"] = (purpose["Refused"]*100)/(purpose["Approved"]+purpose["Canceled"]+purpose["Refused"]+purpose["Unused offer"])
purpose["u"] = (purpose["Unused offer"]*100)/(purpose["Approved"]+purpose["Canceled"]+purpose["Refused"]+purpose["Unused offer"])
purpose_new = purpose[["a","c","r","u"]]
purpose_new = purpose_new.stack().reset_index()
purpose_new["NAME_CONTRACT_STATUS"] = purpose_new["NAME_CONTRACT_STATUS"].replace({"a":"accepted_percentage","c":"cancelled_percentage",
"r":"refused_percentage","u":"unused_percentage"})
lst = purpose_new["NAME_CONTRACT_STATUS"].unique().tolist()
length = len(lst)
cs = ["lime","orange","r","b"]
fig = plt.figure(figsize=(14,18))
fig.set_facecolor("lightgrey")
for i,j,k in itertools.zip_longest(lst,range(length),cs):
plt.subplot(2,2,j+1)
dat = purpose_new[purpose_new["NAME_CONTRACT_STATUS"] == i]
ax = sns.barplot(0,"NAME_CASH_LOAN_PURPOSE",data=dat.sort_values(by=0,ascending=False),color=k)
plt.ylabel("")
plt.xlabel("percentage")
plt.title(i+" by purpose")
plt.subplots_adjust(wspace = .7)
ax.set_facecolor("k")
plt.figure(figsize=(13,6))
sns.violinplot(y= previous_application["DAYS_DECISION"],
x = previous_application["NAME_CONTRACT_STATUS"],palette=["r","g","b","y"])
plt.axhline(previous_application[previous_application["NAME_CONTRACT_STATUS"] == "Approved"]["DAYS_DECISION"].mean(),
color="r",linestyle="dashed",label="accepted_average")
plt.axhline(previous_application[previous_application["NAME_CONTRACT_STATUS"] == "Refused"]["DAYS_DECISION"].mean(),
color="g",linestyle="dashed",label="refused_average")
plt.axhline(previous_application[previous_application["NAME_CONTRACT_STATUS"] == "Cancelled"]["DAYS_DECISION"].mean(),color="b",
linestyle="dashed",label="cancelled_average")
plt.axhline(previous_application[previous_application["NAME_CONTRACT_STATUS"] == "Unused offer"]["DAYS_DECISION"].mean(),color="y",
linestyle="dashed",label="un used_average")
plt.legend(loc="best")
plt.title("Contract status relative to decision made about previous application.")
plt.show()
plt.figure(figsize=(8,12))
plt.subplot(211)
rej = previous_application["CODE_REJECT_REASON"].value_counts().reset_index()
ax = sns.barplot("CODE_REJECT_REASON","index",data=rej[:6],palette="husl")
for i,j in enumerate(np.around((rej["CODE_REJECT_REASON"][:6].values*100/(rej["CODE_REJECT_REASON"][:6].sum())))):
ax.text(.7,i,j,weight="bold")
plt.xlabel("Top as percentage & Bottom as Count")
plt.ylabel("CODE_REJECT_REASON")
plt.title("Reasons for application rejections")
plt.subplot(212)
pay = previous_application["NAME_PAYMENT_TYPE"].value_counts().reset_index()
ax1 = sns.barplot("NAME_PAYMENT_TYPE","index",data=pay,palette="husl")
for i,j in enumerate(np.around((pay["NAME_PAYMENT_TYPE"].values*100/(pay["NAME_PAYMENT_TYPE"].sum())))):
ax1.text(.7,i,j,weight="bold")
plt.xlabel("pTop as percentage & Bottom as Count")
plt.ylabel("NAME_PAYMENT_TYPE")
plt.title("Clients payment methods")
plt.subplots_adjust(hspace = .3)
plt.figure(figsize=(20,20))
plt.subplot(121)
previous_application["NAME_TYPE_SUITE"].value_counts().plot.pie(autopct = "%1.1f%%",fontsize=12,
colors = sns.color_palette("inferno"),
wedgeprops={"linewidth":2,"edgecolor":"white"},shadow =True)
circ = plt.Circle((0,0),.7,color="white")
plt.gca().add_artist(circ)
plt.title("NAME_TYPE_SUITE")
plt.subplot(122)
previous_application["NAME_CLIENT_TYPE"].value_counts().plot.pie(autopct = "%1.1f%%",fontsize=12,
colors = sns.color_palette("inferno"),
wedgeprops={"linewidth":2,"edgecolor":"white"},shadow =True)
circ = plt.Circle((0,0),.7,color="white")
plt.gca().add_artist(circ)
plt.title("NAME_CLIENT_TYPE")
plt.show()
goods = previous_application["NAME_GOODS_CATEGORY"].value_counts().reset_index()
goods["percentage"] = round(goods["NAME_GOODS_CATEGORY"]*100/goods["NAME_GOODS_CATEGORY"].sum(),2)
fig = plt.figure(figsize=(12,5))
ax = sns.pointplot("index","percentage",data=goods,color="yellow")
plt.xticks(rotation = 80)
plt.xlabel("NAME_GOODS_CATEGORY")
plt.ylabel("percentage")
plt.title("popular goods for applying loans")
ax.set_facecolor("k")
fig.set_facecolor('lightgrey')
plt.figure(figsize=(20,20))
plt.subplot(121)
previous_application["NAME_PORTFOLIO"].value_counts().plot.pie(autopct = "%1.1f%%",fontsize=12,
colors = sns.color_palette("prism",5),
wedgeprops={"linewidth":2,"edgecolor":"white"},
shadow =True)
plt.title("previous applications portfolio")
plt.subplot(122)
previous_application["NAME_PRODUCT_TYPE"].value_counts().plot.pie(autopct = "%1.1f%%",fontsize=12,
colors = sns.color_palette("prism",3),
wedgeprops={"linewidth":2,"edgecolor":"white"},
shadow =True)
plt.title("previous applications product types")
plt.show()
app = pd.crosstab(previous_application["CHANNEL_TYPE"],previous_application["NAME_CONTRACT_STATUS"])
app1 = app
app1["approval_rate"] = app1["Approved"]*100/(app1["Approved"]+app1["Refused"]+app1["Canceled"])
app1["refused_rate"] = app1["Refused"]*100/(app1["Approved"]+app1["Refused"]+app1["Canceled"])
app1["cacelled_rate"] = app1["Canceled"]*100/(app1["Approved"]+app1["Refused"]+app1["Canceled"])
app2 = app[["approval_rate","refused_rate","cacelled_rate"]]
ax = app2.plot(kind="barh",stacked=True,figsize=(10,7))
ax.set_facecolor("k")
ax.set_xlabel("percentage")
ax.set_title("approval,cancel and refusal rates by channel types")
plt.show()
fig = plt.figure(figsize=(13,5))
plt.subplot(121)
are = previous_application.groupby("SELLERPLACE_AREA")["AMT_CREDIT"].sum().reset_index()
are = are.sort_values(by ="AMT_CREDIT",ascending = False)
ax = sns.barplot(y= "AMT_CREDIT",x ="SELLERPLACE_AREA",data=are[:15],color="r")
ax.set_facecolor("k")
ax.set_title("Highest amount credited seller place areas")
plt.subplot(122)
sell = previous_application.groupby("NAME_SELLER_INDUSTRY")["AMT_CREDIT"].sum().reset_index().sort_values(by = "AMT_CREDIT",ascending = False)
ax1=sns.barplot(y = "AMT_CREDIT",x = "NAME_SELLER_INDUSTRY",data=sell,color="b")
ax1.set_facecolor("k")
ax1.set_title("Highest amount credited seller industrys")
plt.xticks(rotation=90)
plt.subplots_adjust(wspace = .5)
fig.set_facecolor("lightgrey")
plt.figure(figsize=(13,5))
ax = sns.countplot(previous_application["CNT_PAYMENT"],palette="Set1",order=previous_application["CNT_PAYMENT"].value_counts().index)
ax.set_facecolor("k")
plt.xticks(rotation = 90)
plt.title("popular terms of previous credit at application")
plt.show()
plt.figure(figsize=(10,8))
sns.countplot(y = previous_application["PRODUCT_COMBINATION"],order=previous_application["PRODUCT_COMBINATION"].value_counts().index)
plt.title("Detailed product combination of the previous application -count")
plt.show()
plt.figure(figsize=(12,6))
plt.subplot(121)
previous_application["NFLAG_INSURED_ON_APPROVAL"].value_counts().plot.pie(autopct = "%1.1f%%",fontsize=8,
colors = sns.color_palette("prism",4),
wedgeprops={"linewidth":2,"edgecolor":"white"},shadow =True)
circ = plt.Circle((0,0),.7,color="white")
plt.gca().add_artist(circ)
plt.title("client requesting insurance")
plt.subplot(122)
previous_application["NAME_YIELD_GROUP"].value_counts().plot.pie(autopct = "%1.1f%%",fontsize=8,
colors = sns.color_palette("prism",4),
wedgeprops={"linewidth":2,"edgecolor":"white"},shadow =True)
circ = plt.Circle((0,0),.7,color="white")
plt.gca().add_artist(circ)
plt.title("interest rates")
plt.show()
cols = ['DAYS_FIRST_DRAWING', 'DAYS_FIRST_DUE', 'DAYS_LAST_DUE_1ST_VERSION','DAYS_LAST_DUE', 'DAYS_TERMINATION']
plt.figure(figsize=(12,6))
sns.heatmap(previous_application[cols].describe()[1:].transpose(),
annot=True,linewidth=2,linecolor="k",cmap=sns.color_palette("inferno"))
plt.show()
corrmat = application_data.corr()
f, ax = plt.subplots(figsize =(8, 8))
sns.heatmap(corrmat, ax = ax, cmap ="rainbow")
plt.show()
corrmat = previous_application.corr()
f, ax = plt.subplots(figsize =(8, 8))
sns.heatmap(corrmat, ax = ax, cmap ="rainbow")
plt.show()
corrmat = previous_application.corr()
corrdf = corrmat.where(np.triu(np.ones(corrmat.shape), k=1).astype(np.bool))
corrdf = corrdf.unstack().reset_index()
corrdf.columns = ['Var1', 'Var2', 'Correlation']
corrdf.dropna(subset = ['Correlation'], inplace = True)
corrdf['Correlation'] = round(corrdf['Correlation'], 2)
corrdf['Correlation'] = abs(corrdf['Correlation'])
corrdf.sort_values(by = 'Correlation', ascending = False).head(10)
| Var1 | Var2 | Correlation | |
|---|---|---|---|
| 88 | AMT_GOODS_PRICE | AMT_APPLICATION | 1.00 |
| 89 | AMT_GOODS_PRICE | AMT_CREDIT | 0.99 |
| 71 | AMT_CREDIT | AMT_APPLICATION | 0.98 |
| 269 | DAYS_TERMINATION | DAYS_LAST_DUE | 0.93 |
| 87 | AMT_GOODS_PRICE | AMT_ANNUITY | 0.82 |
| 70 | AMT_CREDIT | AMT_ANNUITY | 0.82 |
| 53 | AMT_APPLICATION | AMT_ANNUITY | 0.81 |
| 232 | DAYS_LAST_DUE_1ST_VERSION | DAYS_FIRST_DRAWING | 0.80 |
| 173 | CNT_PAYMENT | AMT_APPLICATION | 0.68 |
| 174 | CNT_PAYMENT | AMT_CREDIT | 0.67 |
df_repayer = application_data[application_data['TARGET'] == 0]
df_defaulter = application_data[application_data['TARGET'] == 1]
corrmat = df_repayer.corr()
corrdf = corrmat.where(np.triu(np.ones(corrmat.shape), k=1).astype(np.bool))
corrdf = corrdf.unstack().reset_index()
corrdf.columns = ['Var1', 'Var2', 'Correlation']
corrdf.dropna(subset = ['Correlation'], inplace = True)
corrdf['Correlation'] = round(corrdf['Correlation'], 2)
corrdf['Correlation'] = abs(corrdf['Correlation'])
corrdf.sort_values(by = 'Correlation', ascending = False).head(10)
| Var1 | Var2 | Correlation | |
|---|---|---|---|
| 776 | FLAG_EMP_PHONE | DAYS_EMPLOYED | 1.00 |
| 1798 | OBS_60_CNT_SOCIAL_CIRCLE | OBS_30_CNT_SOCIAL_CIRCLE | 1.00 |
| 358 | AMT_GOODS_PRICE | AMT_CREDIT | 0.99 |
| 1199 | REGION_RATING_CLIENT_W_CITY | REGION_RATING_CLIENT | 0.95 |
| 1064 | CNT_FAM_MEMBERS | CNT_CHILDREN | 0.88 |
| 1858 | DEF_60_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | 0.86 |
| 1439 | LIVE_REGION_NOT_WORK_REGION | REG_REGION_NOT_WORK_REGION | 0.86 |
| 1619 | LIVE_CITY_NOT_WORK_CITY | REG_CITY_NOT_WORK_CITY | 0.83 |
| 359 | AMT_GOODS_PRICE | AMT_ANNUITY | 0.78 |
| 299 | AMT_ANNUITY | AMT_CREDIT | 0.77 |
corrmat = df_defaulter.corr()
corrdf = corrmat.where(np.triu(np.ones(corrmat.shape), k=1).astype(np.bool))
corrdf = corrdf.unstack().reset_index()
corrdf.columns = ['Var1', 'Var2', 'Correlation']
corrdf.dropna(subset = ['Correlation'], inplace = True)
corrdf['Correlation'] = round(corrdf['Correlation'], 2)
corrdf['Correlation'] = abs(corrdf['Correlation'])
corrdf.sort_values(by = 'Correlation', ascending = False).head(10)
| Var1 | Var2 | Correlation | |
|---|---|---|---|
| 1798 | OBS_60_CNT_SOCIAL_CIRCLE | OBS_30_CNT_SOCIAL_CIRCLE | 1.00 |
| 776 | FLAG_EMP_PHONE | DAYS_EMPLOYED | 1.00 |
| 358 | AMT_GOODS_PRICE | AMT_CREDIT | 0.98 |
| 1199 | REGION_RATING_CLIENT_W_CITY | REGION_RATING_CLIENT | 0.96 |
| 1064 | CNT_FAM_MEMBERS | CNT_CHILDREN | 0.89 |
| 1858 | DEF_60_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | 0.87 |
| 1439 | LIVE_REGION_NOT_WORK_REGION | REG_REGION_NOT_WORK_REGION | 0.85 |
| 1619 | LIVE_CITY_NOT_WORK_CITY | REG_CITY_NOT_WORK_CITY | 0.78 |
| 299 | AMT_ANNUITY | AMT_CREDIT | 0.75 |
| 359 | AMT_GOODS_PRICE | AMT_ANNUITY | 0.75 |
mergeddf = pd.merge(application_data,previous_application,on='SK_ID_CURR')
mergeddf.head()
| SK_ID_CURR | TARGET | NAME_CONTRACT_TYPE_x | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT_x | AMT_ANNUITY_x | AMT_GOODS_PRICE_x | NAME_TYPE_SUITE_x | NAME_INCOME_TYPE | NAME_EDUCATION_TYPE | NAME_FAMILY_STATUS | NAME_HOUSING_TYPE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | WEEKDAY_APPR_PROCESS_START_x | HOUR_APPR_PROCESS_START_x | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | ORGANIZATION_TYPE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | SK_ID_PREV | NAME_CONTRACT_TYPE_y | AMT_ANNUITY_y | AMT_APPLICATION | AMT_CREDIT_y | AMT_GOODS_PRICE_y | WEEKDAY_APPR_PROCESS_START_y | HOUR_APPR_PROCESS_START_y | FLAG_LAST_APPL_PER_CONTRACT | NFLAG_LAST_APPL_IN_DAY | NAME_CASH_LOAN_PURPOSE | NAME_CONTRACT_STATUS | DAYS_DECISION | NAME_PAYMENT_TYPE | CODE_REJECT_REASON | NAME_TYPE_SUITE_y | NAME_CLIENT_TYPE | NAME_GOODS_CATEGORY | NAME_PORTFOLIO | NAME_PRODUCT_TYPE | CHANNEL_TYPE | SELLERPLACE_AREA | NAME_SELLER_INDUSTRY | CNT_PAYMENT | NAME_YIELD_GROUP | PRODUCT_COMBINATION | DAYS_FIRST_DRAWING | DAYS_FIRST_DUE | DAYS_LAST_DUE_1ST_VERSION | DAYS_LAST_DUE | DAYS_TERMINATION | NFLAG_INSURED_ON_APPROVAL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 100002 | 1 | Cash loans | M | N | Y | 0 | 202500.0 | 406597.5 | 24700.5 | 351000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.018801 | 9461 | -637 | -3648.0 | 2120 | 1 | 1 | 0 | 1 | 1 | 0 | 1.0 | 2 | 2 | WEDNESDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 2.0 | 2.0 | 2.0 | 2.0 | 1134.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1038818 | Consumer loans | 9251.775 | 179055.0 | 179055.0 | 179055.0 | SATURDAY | 9 | Y | 1 | XAP | Approved | -606 | XNA | XAP | NaN | New | Vehicles | POS | XNA | Stone | 500 | Auto technology | 24.0 | low_normal | POS other with interest | 365243.0 | -565.0 | 125.0 | -25.0 | -17.0 | 0.0 |
| 1 | 100003 | 0 | Cash loans | F | N | N | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1810518 | Cash loans | 98356.995 | 900000.0 | 1035882.0 | 900000.0 | FRIDAY | 12 | Y | 1 | XNA | Approved | -746 | XNA | XAP | Unaccompanied | Repeater | XNA | Cash | x-sell | Credit and cash offices | -1 | XNA | 12.0 | low_normal | Cash X-Sell: low | 365243.0 | -716.0 | -386.0 | -536.0 | -527.0 | 1.0 |
| 2 | 100003 | 0 | Cash loans | F | N | N | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2636178 | Consumer loans | 64567.665 | 337500.0 | 348637.5 | 337500.0 | SUNDAY | 17 | Y | 1 | XAP | Approved | -828 | Cash through the bank | XAP | Family | Refreshed | Furniture | POS | XNA | Stone | 1400 | Furniture | 6.0 | middle | POS industry with interest | 365243.0 | -797.0 | -647.0 | -647.0 | -639.0 | 0.0 |
| 3 | 100003 | 0 | Cash loans | F | N | N | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2396755 | Consumer loans | 6737.310 | 68809.5 | 68053.5 | 68809.5 | SATURDAY | 15 | Y | 1 | XAP | Approved | -2341 | Cash through the bank | XAP | Family | Refreshed | Consumer Electronics | POS | XNA | Country-wide | 200 | Consumer electronics | 12.0 | middle | POS household with interest | 365243.0 | -2310.0 | -1980.0 | -1980.0 | -1976.0 | 1.0 |
| 4 | 100004 | 0 | Revolving loans | M | Y | Y | 0 | 67500.0 | 135000.0 | 6750.0 | 135000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.010032 | 19046 | -225 | -4260.0 | 2531 | 1 | 1 | 1 | 1 | 1 | 0 | 1.0 | 2 | 2 | MONDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Government | 0.0 | 0.0 | 0.0 | 0.0 | 815.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1564014 | Consumer loans | 5357.250 | 24282.0 | 20106.0 | 24282.0 | FRIDAY | 5 | Y | 1 | XAP | Approved | -815 | Cash through the bank | XAP | Unaccompanied | New | Mobile | POS | XNA | Regional / Local | 30 | Connectivity | 4.0 | middle | POS mobile without interest | 365243.0 | -784.0 | -694.0 | -724.0 | -714.0 | 0.0 |
y = mergeddf.groupby('SK_ID_CURR').size()
dfA = mergeddf.groupby('SK_ID_CURR').agg({'TARGET': np.sum})
dfA['count'] = y
display(dfA.head(10))
| TARGET | count | |
|---|---|---|
| SK_ID_CURR | ||
| 100002 | 1 | 1 |
| 100003 | 0 | 3 |
| 100004 | 0 | 1 |
| 100006 | 0 | 9 |
| 100007 | 0 | 6 |
| 100008 | 0 | 5 |
| 100009 | 0 | 7 |
| 100010 | 0 | 1 |
| 100011 | 0 | 4 |
| 100012 | 0 | 4 |
dfA.sort_values(by = 'count',ascending=False).head(10)
| TARGET | count | |
|---|---|---|
| SK_ID_CURR | ||
| 265681 | 0 | 73 |
| 173680 | 0 | 72 |
| 242412 | 0 | 68 |
| 206783 | 0 | 67 |
| 389950 | 0 | 64 |
| 382179 | 0 | 64 |
| 198355 | 0 | 63 |
| 345161 | 0 | 62 |
| 446486 | 0 | 62 |
| 238250 | 0 | 61 |
df_repayer = dfA[dfA['TARGET'] == 0]
df_defaulter = dfA[dfA['TARGET'] == 1]
df_repayer.sort_values(by = 'count',ascending=False).head(10)
| TARGET | count | |
|---|---|---|
| SK_ID_CURR | ||
| 265681 | 0 | 73 |
| 173680 | 0 | 72 |
| 242412 | 0 | 68 |
| 206783 | 0 | 67 |
| 382179 | 0 | 64 |
| 389950 | 0 | 64 |
| 198355 | 0 | 63 |
| 446486 | 0 | 62 |
| 345161 | 0 | 62 |
| 280586 | 0 | 61 |
df_defaulter.sort_values(by = 'count',ascending=False).head(10)
| TARGET | count | |
|---|---|---|
| SK_ID_CURR | ||
| 100002 | 1 | 1 |
| 333349 | 1 | 1 |
| 333587 | 1 | 1 |
| 333582 | 1 | 1 |
| 333534 | 1 | 1 |
| 333506 | 1 | 1 |
| 333419 | 1 | 1 |
| 333355 | 1 | 1 |
| 333337 | 1 | 1 |
| 334761 | 1 | 1 |
mergeddf.isnull().sum()
SK_ID_CURR 0 TARGET 0 NAME_CONTRACT_TYPE_x 0 CODE_GENDER 0 FLAG_OWN_CAR 0 FLAG_OWN_REALTY 0 CNT_CHILDREN 0 AMT_INCOME_TOTAL 0 AMT_CREDIT_x 0 AMT_ANNUITY_x 93 AMT_GOODS_PRICE_x 1208 NAME_TYPE_SUITE_x 3526 NAME_INCOME_TYPE 0 NAME_EDUCATION_TYPE 0 NAME_FAMILY_STATUS 0 NAME_HOUSING_TYPE 0 REGION_POPULATION_RELATIVE 0 DAYS_BIRTH 0 DAYS_EMPLOYED 0 DAYS_REGISTRATION 0 DAYS_ID_PUBLISH 0 FLAG_MOBIL 0 FLAG_EMP_PHONE 0 FLAG_WORK_PHONE 0 FLAG_CONT_MOBILE 0 FLAG_PHONE 0 FLAG_EMAIL 0 CNT_FAM_MEMBERS 0 REGION_RATING_CLIENT 0 REGION_RATING_CLIENT_W_CITY 0 WEEKDAY_APPR_PROCESS_START_x 0 HOUR_APPR_PROCESS_START_x 0 REG_REGION_NOT_LIVE_REGION 0 REG_REGION_NOT_WORK_REGION 0 LIVE_REGION_NOT_WORK_REGION 0 REG_CITY_NOT_LIVE_CITY 0 REG_CITY_NOT_WORK_CITY 0 LIVE_CITY_NOT_WORK_CITY 0 ORGANIZATION_TYPE 0 OBS_30_CNT_SOCIAL_CIRCLE 3146 DEF_30_CNT_SOCIAL_CIRCLE 3146 OBS_60_CNT_SOCIAL_CIRCLE 3146 DEF_60_CNT_SOCIAL_CIRCLE 3146 DAYS_LAST_PHONE_CHANGE 0 FLAG_DOCUMENT_2 0 FLAG_DOCUMENT_3 0 FLAG_DOCUMENT_4 0 FLAG_DOCUMENT_5 0 FLAG_DOCUMENT_6 0 FLAG_DOCUMENT_7 0 FLAG_DOCUMENT_8 0 FLAG_DOCUMENT_9 0 FLAG_DOCUMENT_10 0 FLAG_DOCUMENT_11 0 FLAG_DOCUMENT_12 0 FLAG_DOCUMENT_13 0 FLAG_DOCUMENT_14 0 FLAG_DOCUMENT_15 0 FLAG_DOCUMENT_16 0 FLAG_DOCUMENT_17 0 FLAG_DOCUMENT_18 0 FLAG_DOCUMENT_19 0 FLAG_DOCUMENT_20 0 FLAG_DOCUMENT_21 0 AMT_REQ_CREDIT_BUREAU_HOUR 163627 AMT_REQ_CREDIT_BUREAU_DAY 163627 AMT_REQ_CREDIT_BUREAU_WEEK 163627 AMT_REQ_CREDIT_BUREAU_MON 163627 AMT_REQ_CREDIT_BUREAU_QRT 163627 AMT_REQ_CREDIT_BUREAU_YEAR 163627 SK_ID_PREV 0 NAME_CONTRACT_TYPE_y 0 AMT_ANNUITY_y 307218 AMT_APPLICATION 0 AMT_CREDIT_y 1 AMT_GOODS_PRICE_y 319525 WEEKDAY_APPR_PROCESS_START_y 0 HOUR_APPR_PROCESS_START_y 0 FLAG_LAST_APPL_PER_CONTRACT 0 NFLAG_LAST_APPL_IN_DAY 0 NAME_CASH_LOAN_PURPOSE 0 NAME_CONTRACT_STATUS 0 DAYS_DECISION 0 NAME_PAYMENT_TYPE 0 CODE_REJECT_REASON 0 NAME_TYPE_SUITE_y 694672 NAME_CLIENT_TYPE 0 NAME_GOODS_CATEGORY 0 NAME_PORTFOLIO 0 NAME_PRODUCT_TYPE 0 CHANNEL_TYPE 0 SELLERPLACE_AREA 0 NAME_SELLER_INDUSTRY 0 CNT_PAYMENT 307213 NAME_YIELD_GROUP 0 PRODUCT_COMBINATION 313 DAYS_FIRST_DRAWING 561106 DAYS_FIRST_DUE 561106 DAYS_LAST_DUE_1ST_VERSION 561106 DAYS_LAST_DUE 561106 DAYS_TERMINATION 561106 NFLAG_INSURED_ON_APPROVAL 561106 dtype: int64
round(100*(mergeddf.isnull().sum()/len(mergeddf.index)), 2)
SK_ID_CURR 0.00 TARGET 0.00 NAME_CONTRACT_TYPE_x 0.00 CODE_GENDER 0.00 FLAG_OWN_CAR 0.00 FLAG_OWN_REALTY 0.00 CNT_CHILDREN 0.00 AMT_INCOME_TOTAL 0.00 AMT_CREDIT_x 0.00 AMT_ANNUITY_x 0.01 AMT_GOODS_PRICE_x 0.09 NAME_TYPE_SUITE_x 0.25 NAME_INCOME_TYPE 0.00 NAME_EDUCATION_TYPE 0.00 NAME_FAMILY_STATUS 0.00 NAME_HOUSING_TYPE 0.00 REGION_POPULATION_RELATIVE 0.00 DAYS_BIRTH 0.00 DAYS_EMPLOYED 0.00 DAYS_REGISTRATION 0.00 DAYS_ID_PUBLISH 0.00 FLAG_MOBIL 0.00 FLAG_EMP_PHONE 0.00 FLAG_WORK_PHONE 0.00 FLAG_CONT_MOBILE 0.00 FLAG_PHONE 0.00 FLAG_EMAIL 0.00 CNT_FAM_MEMBERS 0.00 REGION_RATING_CLIENT 0.00 REGION_RATING_CLIENT_W_CITY 0.00 WEEKDAY_APPR_PROCESS_START_x 0.00 HOUR_APPR_PROCESS_START_x 0.00 REG_REGION_NOT_LIVE_REGION 0.00 REG_REGION_NOT_WORK_REGION 0.00 LIVE_REGION_NOT_WORK_REGION 0.00 REG_CITY_NOT_LIVE_CITY 0.00 REG_CITY_NOT_WORK_CITY 0.00 LIVE_CITY_NOT_WORK_CITY 0.00 ORGANIZATION_TYPE 0.00 OBS_30_CNT_SOCIAL_CIRCLE 0.22 DEF_30_CNT_SOCIAL_CIRCLE 0.22 OBS_60_CNT_SOCIAL_CIRCLE 0.22 DEF_60_CNT_SOCIAL_CIRCLE 0.22 DAYS_LAST_PHONE_CHANGE 0.00 FLAG_DOCUMENT_2 0.00 FLAG_DOCUMENT_3 0.00 FLAG_DOCUMENT_4 0.00 FLAG_DOCUMENT_5 0.00 FLAG_DOCUMENT_6 0.00 FLAG_DOCUMENT_7 0.00 FLAG_DOCUMENT_8 0.00 FLAG_DOCUMENT_9 0.00 FLAG_DOCUMENT_10 0.00 FLAG_DOCUMENT_11 0.00 FLAG_DOCUMENT_12 0.00 FLAG_DOCUMENT_13 0.00 FLAG_DOCUMENT_14 0.00 FLAG_DOCUMENT_15 0.00 FLAG_DOCUMENT_16 0.00 FLAG_DOCUMENT_17 0.00 FLAG_DOCUMENT_18 0.00 FLAG_DOCUMENT_19 0.00 FLAG_DOCUMENT_20 0.00 FLAG_DOCUMENT_21 0.00 AMT_REQ_CREDIT_BUREAU_HOUR 11.57 AMT_REQ_CREDIT_BUREAU_DAY 11.57 AMT_REQ_CREDIT_BUREAU_WEEK 11.57 AMT_REQ_CREDIT_BUREAU_MON 11.57 AMT_REQ_CREDIT_BUREAU_QRT 11.57 AMT_REQ_CREDIT_BUREAU_YEAR 11.57 SK_ID_PREV 0.00 NAME_CONTRACT_TYPE_y 0.00 AMT_ANNUITY_y 21.73 AMT_APPLICATION 0.00 AMT_CREDIT_y 0.00 AMT_GOODS_PRICE_y 22.60 WEEKDAY_APPR_PROCESS_START_y 0.00 HOUR_APPR_PROCESS_START_y 0.00 FLAG_LAST_APPL_PER_CONTRACT 0.00 NFLAG_LAST_APPL_IN_DAY 0.00 NAME_CASH_LOAN_PURPOSE 0.00 NAME_CONTRACT_STATUS 0.00 DAYS_DECISION 0.00 NAME_PAYMENT_TYPE 0.00 CODE_REJECT_REASON 0.00 NAME_TYPE_SUITE_y 49.14 NAME_CLIENT_TYPE 0.00 NAME_GOODS_CATEGORY 0.00 NAME_PORTFOLIO 0.00 NAME_PRODUCT_TYPE 0.00 CHANNEL_TYPE 0.00 SELLERPLACE_AREA 0.00 NAME_SELLER_INDUSTRY 0.00 CNT_PAYMENT 21.73 NAME_YIELD_GROUP 0.00 PRODUCT_COMBINATION 0.02 DAYS_FIRST_DRAWING 39.69 DAYS_FIRST_DUE 39.69 DAYS_LAST_DUE_1ST_VERSION 39.69 DAYS_LAST_DUE 39.69 DAYS_TERMINATION 39.69 NFLAG_INSURED_ON_APPROVAL 39.69 dtype: float64
mergeddf.head()
| SK_ID_CURR | TARGET | NAME_CONTRACT_TYPE_x | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT_x | AMT_ANNUITY_x | AMT_GOODS_PRICE_x | NAME_TYPE_SUITE_x | NAME_INCOME_TYPE | NAME_EDUCATION_TYPE | NAME_FAMILY_STATUS | NAME_HOUSING_TYPE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | WEEKDAY_APPR_PROCESS_START_x | HOUR_APPR_PROCESS_START_x | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | ORGANIZATION_TYPE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | SK_ID_PREV | NAME_CONTRACT_TYPE_y | AMT_ANNUITY_y | AMT_APPLICATION | AMT_CREDIT_y | AMT_GOODS_PRICE_y | WEEKDAY_APPR_PROCESS_START_y | HOUR_APPR_PROCESS_START_y | FLAG_LAST_APPL_PER_CONTRACT | NFLAG_LAST_APPL_IN_DAY | NAME_CASH_LOAN_PURPOSE | NAME_CONTRACT_STATUS | DAYS_DECISION | NAME_PAYMENT_TYPE | CODE_REJECT_REASON | NAME_TYPE_SUITE_y | NAME_CLIENT_TYPE | NAME_GOODS_CATEGORY | NAME_PORTFOLIO | NAME_PRODUCT_TYPE | CHANNEL_TYPE | SELLERPLACE_AREA | NAME_SELLER_INDUSTRY | CNT_PAYMENT | NAME_YIELD_GROUP | PRODUCT_COMBINATION | DAYS_FIRST_DRAWING | DAYS_FIRST_DUE | DAYS_LAST_DUE_1ST_VERSION | DAYS_LAST_DUE | DAYS_TERMINATION | NFLAG_INSURED_ON_APPROVAL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 100002 | 1 | Cash loans | M | N | Y | 0 | 202500.0 | 406597.5 | 24700.5 | 351000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.018801 | 9461 | -637 | -3648.0 | 2120 | 1 | 1 | 0 | 1 | 1 | 0 | 1.0 | 2 | 2 | WEDNESDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 2.0 | 2.0 | 2.0 | 2.0 | 1134.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1038818 | Consumer loans | 9251.775 | 179055.0 | 179055.0 | 179055.0 | SATURDAY | 9 | Y | 1 | XAP | Approved | -606 | XNA | XAP | NaN | New | Vehicles | POS | XNA | Stone | 500 | Auto technology | 24.0 | low_normal | POS other with interest | 365243.0 | -565.0 | 125.0 | -25.0 | -17.0 | 0.0 |
| 1 | 100003 | 0 | Cash loans | F | N | N | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1810518 | Cash loans | 98356.995 | 900000.0 | 1035882.0 | 900000.0 | FRIDAY | 12 | Y | 1 | XNA | Approved | -746 | XNA | XAP | Unaccompanied | Repeater | XNA | Cash | x-sell | Credit and cash offices | -1 | XNA | 12.0 | low_normal | Cash X-Sell: low | 365243.0 | -716.0 | -386.0 | -536.0 | -527.0 | 1.0 |
| 2 | 100003 | 0 | Cash loans | F | N | N | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2636178 | Consumer loans | 64567.665 | 337500.0 | 348637.5 | 337500.0 | SUNDAY | 17 | Y | 1 | XAP | Approved | -828 | Cash through the bank | XAP | Family | Refreshed | Furniture | POS | XNA | Stone | 1400 | Furniture | 6.0 | middle | POS industry with interest | 365243.0 | -797.0 | -647.0 | -647.0 | -639.0 | 0.0 |
| 3 | 100003 | 0 | Cash loans | F | N | N | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2396755 | Consumer loans | 6737.310 | 68809.5 | 68053.5 | 68809.5 | SATURDAY | 15 | Y | 1 | XAP | Approved | -2341 | Cash through the bank | XAP | Family | Refreshed | Consumer Electronics | POS | XNA | Country-wide | 200 | Consumer electronics | 12.0 | middle | POS household with interest | 365243.0 | -2310.0 | -1980.0 | -1980.0 | -1976.0 | 1.0 |
| 4 | 100004 | 0 | Revolving loans | M | Y | Y | 0 | 67500.0 | 135000.0 | 6750.0 | 135000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.010032 | 19046 | -225 | -4260.0 | 2531 | 1 | 1 | 1 | 1 | 1 | 0 | 1.0 | 2 | 2 | MONDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Government | 0.0 | 0.0 | 0.0 | 0.0 | 815.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1564014 | Consumer loans | 5357.250 | 24282.0 | 20106.0 | 24282.0 | FRIDAY | 5 | Y | 1 | XAP | Approved | -815 | Cash through the bank | XAP | Unaccompanied | New | Mobile | POS | XNA | Regional / Local | 30 | Connectivity | 4.0 | middle | POS mobile without interest | 365243.0 | -784.0 | -694.0 | -724.0 | -714.0 | 0.0 |
mergeddf.drop(['SK_ID_CURR'], 1, inplace = True)
mergeddf.head()
| TARGET | NAME_CONTRACT_TYPE_x | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT_x | AMT_ANNUITY_x | AMT_GOODS_PRICE_x | NAME_TYPE_SUITE_x | NAME_INCOME_TYPE | NAME_EDUCATION_TYPE | NAME_FAMILY_STATUS | NAME_HOUSING_TYPE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | WEEKDAY_APPR_PROCESS_START_x | HOUR_APPR_PROCESS_START_x | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | ORGANIZATION_TYPE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | SK_ID_PREV | NAME_CONTRACT_TYPE_y | AMT_ANNUITY_y | AMT_APPLICATION | AMT_CREDIT_y | AMT_GOODS_PRICE_y | WEEKDAY_APPR_PROCESS_START_y | HOUR_APPR_PROCESS_START_y | FLAG_LAST_APPL_PER_CONTRACT | NFLAG_LAST_APPL_IN_DAY | NAME_CASH_LOAN_PURPOSE | NAME_CONTRACT_STATUS | DAYS_DECISION | NAME_PAYMENT_TYPE | CODE_REJECT_REASON | NAME_TYPE_SUITE_y | NAME_CLIENT_TYPE | NAME_GOODS_CATEGORY | NAME_PORTFOLIO | NAME_PRODUCT_TYPE | CHANNEL_TYPE | SELLERPLACE_AREA | NAME_SELLER_INDUSTRY | CNT_PAYMENT | NAME_YIELD_GROUP | PRODUCT_COMBINATION | DAYS_FIRST_DRAWING | DAYS_FIRST_DUE | DAYS_LAST_DUE_1ST_VERSION | DAYS_LAST_DUE | DAYS_TERMINATION | NFLAG_INSURED_ON_APPROVAL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Cash loans | M | N | Y | 0 | 202500.0 | 406597.5 | 24700.5 | 351000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.018801 | 9461 | -637 | -3648.0 | 2120 | 1 | 1 | 0 | 1 | 1 | 0 | 1.0 | 2 | 2 | WEDNESDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 2.0 | 2.0 | 2.0 | 2.0 | 1134.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1038818 | Consumer loans | 9251.775 | 179055.0 | 179055.0 | 179055.0 | SATURDAY | 9 | Y | 1 | XAP | Approved | -606 | XNA | XAP | NaN | New | Vehicles | POS | XNA | Stone | 500 | Auto technology | 24.0 | low_normal | POS other with interest | 365243.0 | -565.0 | 125.0 | -25.0 | -17.0 | 0.0 |
| 1 | 0 | Cash loans | F | N | N | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1810518 | Cash loans | 98356.995 | 900000.0 | 1035882.0 | 900000.0 | FRIDAY | 12 | Y | 1 | XNA | Approved | -746 | XNA | XAP | Unaccompanied | Repeater | XNA | Cash | x-sell | Credit and cash offices | -1 | XNA | 12.0 | low_normal | Cash X-Sell: low | 365243.0 | -716.0 | -386.0 | -536.0 | -527.0 | 1.0 |
| 2 | 0 | Cash loans | F | N | N | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2636178 | Consumer loans | 64567.665 | 337500.0 | 348637.5 | 337500.0 | SUNDAY | 17 | Y | 1 | XAP | Approved | -828 | Cash through the bank | XAP | Family | Refreshed | Furniture | POS | XNA | Stone | 1400 | Furniture | 6.0 | middle | POS industry with interest | 365243.0 | -797.0 | -647.0 | -647.0 | -639.0 | 0.0 |
| 3 | 0 | Cash loans | F | N | N | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2396755 | Consumer loans | 6737.310 | 68809.5 | 68053.5 | 68809.5 | SATURDAY | 15 | Y | 1 | XAP | Approved | -2341 | Cash through the bank | XAP | Family | Refreshed | Consumer Electronics | POS | XNA | Country-wide | 200 | Consumer electronics | 12.0 | middle | POS household with interest | 365243.0 | -2310.0 | -1980.0 | -1980.0 | -1976.0 | 1.0 |
| 4 | 0 | Revolving loans | M | Y | Y | 0 | 67500.0 | 135000.0 | 6750.0 | 135000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.010032 | 19046 | -225 | -4260.0 | 2531 | 1 | 1 | 1 | 1 | 1 | 0 | 1.0 | 2 | 2 | MONDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Government | 0.0 | 0.0 | 0.0 | 0.0 | 815.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1564014 | Consumer loans | 5357.250 | 24282.0 | 20106.0 | 24282.0 | FRIDAY | 5 | Y | 1 | XAP | Approved | -815 | Cash through the bank | XAP | Unaccompanied | New | Mobile | POS | XNA | Regional / Local | 30 | Connectivity | 4.0 | middle | POS mobile without interest | 365243.0 | -784.0 | -694.0 | -724.0 | -714.0 | 0.0 |
round(100*(mergeddf.isnull().sum()/len(mergeddf.index)), 2)
TARGET 0.00 NAME_CONTRACT_TYPE_x 0.00 CODE_GENDER 0.00 FLAG_OWN_CAR 0.00 FLAG_OWN_REALTY 0.00 CNT_CHILDREN 0.00 AMT_INCOME_TOTAL 0.00 AMT_CREDIT_x 0.00 AMT_ANNUITY_x 0.01 AMT_GOODS_PRICE_x 0.09 NAME_TYPE_SUITE_x 0.25 NAME_INCOME_TYPE 0.00 NAME_EDUCATION_TYPE 0.00 NAME_FAMILY_STATUS 0.00 NAME_HOUSING_TYPE 0.00 REGION_POPULATION_RELATIVE 0.00 DAYS_BIRTH 0.00 DAYS_EMPLOYED 0.00 DAYS_REGISTRATION 0.00 DAYS_ID_PUBLISH 0.00 FLAG_MOBIL 0.00 FLAG_EMP_PHONE 0.00 FLAG_WORK_PHONE 0.00 FLAG_CONT_MOBILE 0.00 FLAG_PHONE 0.00 FLAG_EMAIL 0.00 CNT_FAM_MEMBERS 0.00 REGION_RATING_CLIENT 0.00 REGION_RATING_CLIENT_W_CITY 0.00 WEEKDAY_APPR_PROCESS_START_x 0.00 HOUR_APPR_PROCESS_START_x 0.00 REG_REGION_NOT_LIVE_REGION 0.00 REG_REGION_NOT_WORK_REGION 0.00 LIVE_REGION_NOT_WORK_REGION 0.00 REG_CITY_NOT_LIVE_CITY 0.00 REG_CITY_NOT_WORK_CITY 0.00 LIVE_CITY_NOT_WORK_CITY 0.00 ORGANIZATION_TYPE 0.00 OBS_30_CNT_SOCIAL_CIRCLE 0.22 DEF_30_CNT_SOCIAL_CIRCLE 0.22 OBS_60_CNT_SOCIAL_CIRCLE 0.22 DEF_60_CNT_SOCIAL_CIRCLE 0.22 DAYS_LAST_PHONE_CHANGE 0.00 FLAG_DOCUMENT_2 0.00 FLAG_DOCUMENT_3 0.00 FLAG_DOCUMENT_4 0.00 FLAG_DOCUMENT_5 0.00 FLAG_DOCUMENT_6 0.00 FLAG_DOCUMENT_7 0.00 FLAG_DOCUMENT_8 0.00 FLAG_DOCUMENT_9 0.00 FLAG_DOCUMENT_10 0.00 FLAG_DOCUMENT_11 0.00 FLAG_DOCUMENT_12 0.00 FLAG_DOCUMENT_13 0.00 FLAG_DOCUMENT_14 0.00 FLAG_DOCUMENT_15 0.00 FLAG_DOCUMENT_16 0.00 FLAG_DOCUMENT_17 0.00 FLAG_DOCUMENT_18 0.00 FLAG_DOCUMENT_19 0.00 FLAG_DOCUMENT_20 0.00 FLAG_DOCUMENT_21 0.00 AMT_REQ_CREDIT_BUREAU_HOUR 11.57 AMT_REQ_CREDIT_BUREAU_DAY 11.57 AMT_REQ_CREDIT_BUREAU_WEEK 11.57 AMT_REQ_CREDIT_BUREAU_MON 11.57 AMT_REQ_CREDIT_BUREAU_QRT 11.57 AMT_REQ_CREDIT_BUREAU_YEAR 11.57 SK_ID_PREV 0.00 NAME_CONTRACT_TYPE_y 0.00 AMT_ANNUITY_y 21.73 AMT_APPLICATION 0.00 AMT_CREDIT_y 0.00 AMT_GOODS_PRICE_y 22.60 WEEKDAY_APPR_PROCESS_START_y 0.00 HOUR_APPR_PROCESS_START_y 0.00 FLAG_LAST_APPL_PER_CONTRACT 0.00 NFLAG_LAST_APPL_IN_DAY 0.00 NAME_CASH_LOAN_PURPOSE 0.00 NAME_CONTRACT_STATUS 0.00 DAYS_DECISION 0.00 NAME_PAYMENT_TYPE 0.00 CODE_REJECT_REASON 0.00 NAME_TYPE_SUITE_y 49.14 NAME_CLIENT_TYPE 0.00 NAME_GOODS_CATEGORY 0.00 NAME_PORTFOLIO 0.00 NAME_PRODUCT_TYPE 0.00 CHANNEL_TYPE 0.00 SELLERPLACE_AREA 0.00 NAME_SELLER_INDUSTRY 0.00 CNT_PAYMENT 21.73 NAME_YIELD_GROUP 0.00 PRODUCT_COMBINATION 0.02 DAYS_FIRST_DRAWING 39.69 DAYS_FIRST_DUE 39.69 DAYS_LAST_DUE_1ST_VERSION 39.69 DAYS_LAST_DUE 39.69 DAYS_TERMINATION 39.69 NFLAG_INSURED_ON_APPROVAL 39.69 dtype: float64
enq_cs =['AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_HOUR',
'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT',
'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_YEAR']
for i in enq_cs:
mergeddf[i] = mergeddf[i].fillna(0)
amt_cs = ["AMT_ANNUITY_y","AMT_GOODS_PRICE_y"]
for i in amt_cs:
mergeddf[i] = mergeddf[i].fillna(mergeddf[i].mean())
cols = ["DAYS_FIRST_DRAWING","DAYS_FIRST_DUE","DAYS_LAST_DUE_1ST_VERSION",
"DAYS_LAST_DUE","DAYS_TERMINATION",'CNT_PAYMENT']
for i in cols :
mergeddf[i] = mergeddf[i].fillna(mergeddf[i].median())
cols = ["NAME_TYPE_SUITE_y","NFLAG_INSURED_ON_APPROVAL"]
for i in cols :
mergeddf[i] = mergeddf[i].fillna(mergeddf[i].mode()[0])
mergeddf.dropna(inplace = True)
round(100*(mergeddf.isnull().sum()/len(mergeddf.index)), 2)
TARGET 0.0 NAME_CONTRACT_TYPE_x 0.0 CODE_GENDER 0.0 FLAG_OWN_CAR 0.0 FLAG_OWN_REALTY 0.0 CNT_CHILDREN 0.0 AMT_INCOME_TOTAL 0.0 AMT_CREDIT_x 0.0 AMT_ANNUITY_x 0.0 AMT_GOODS_PRICE_x 0.0 NAME_TYPE_SUITE_x 0.0 NAME_INCOME_TYPE 0.0 NAME_EDUCATION_TYPE 0.0 NAME_FAMILY_STATUS 0.0 NAME_HOUSING_TYPE 0.0 REGION_POPULATION_RELATIVE 0.0 DAYS_BIRTH 0.0 DAYS_EMPLOYED 0.0 DAYS_REGISTRATION 0.0 DAYS_ID_PUBLISH 0.0 FLAG_MOBIL 0.0 FLAG_EMP_PHONE 0.0 FLAG_WORK_PHONE 0.0 FLAG_CONT_MOBILE 0.0 FLAG_PHONE 0.0 FLAG_EMAIL 0.0 CNT_FAM_MEMBERS 0.0 REGION_RATING_CLIENT 0.0 REGION_RATING_CLIENT_W_CITY 0.0 WEEKDAY_APPR_PROCESS_START_x 0.0 HOUR_APPR_PROCESS_START_x 0.0 REG_REGION_NOT_LIVE_REGION 0.0 REG_REGION_NOT_WORK_REGION 0.0 LIVE_REGION_NOT_WORK_REGION 0.0 REG_CITY_NOT_LIVE_CITY 0.0 REG_CITY_NOT_WORK_CITY 0.0 LIVE_CITY_NOT_WORK_CITY 0.0 ORGANIZATION_TYPE 0.0 OBS_30_CNT_SOCIAL_CIRCLE 0.0 DEF_30_CNT_SOCIAL_CIRCLE 0.0 OBS_60_CNT_SOCIAL_CIRCLE 0.0 DEF_60_CNT_SOCIAL_CIRCLE 0.0 DAYS_LAST_PHONE_CHANGE 0.0 FLAG_DOCUMENT_2 0.0 FLAG_DOCUMENT_3 0.0 FLAG_DOCUMENT_4 0.0 FLAG_DOCUMENT_5 0.0 FLAG_DOCUMENT_6 0.0 FLAG_DOCUMENT_7 0.0 FLAG_DOCUMENT_8 0.0 FLAG_DOCUMENT_9 0.0 FLAG_DOCUMENT_10 0.0 FLAG_DOCUMENT_11 0.0 FLAG_DOCUMENT_12 0.0 FLAG_DOCUMENT_13 0.0 FLAG_DOCUMENT_14 0.0 FLAG_DOCUMENT_15 0.0 FLAG_DOCUMENT_16 0.0 FLAG_DOCUMENT_17 0.0 FLAG_DOCUMENT_18 0.0 FLAG_DOCUMENT_19 0.0 FLAG_DOCUMENT_20 0.0 FLAG_DOCUMENT_21 0.0 AMT_REQ_CREDIT_BUREAU_HOUR 0.0 AMT_REQ_CREDIT_BUREAU_DAY 0.0 AMT_REQ_CREDIT_BUREAU_WEEK 0.0 AMT_REQ_CREDIT_BUREAU_MON 0.0 AMT_REQ_CREDIT_BUREAU_QRT 0.0 AMT_REQ_CREDIT_BUREAU_YEAR 0.0 SK_ID_PREV 0.0 NAME_CONTRACT_TYPE_y 0.0 AMT_ANNUITY_y 0.0 AMT_APPLICATION 0.0 AMT_CREDIT_y 0.0 AMT_GOODS_PRICE_y 0.0 WEEKDAY_APPR_PROCESS_START_y 0.0 HOUR_APPR_PROCESS_START_y 0.0 FLAG_LAST_APPL_PER_CONTRACT 0.0 NFLAG_LAST_APPL_IN_DAY 0.0 NAME_CASH_LOAN_PURPOSE 0.0 NAME_CONTRACT_STATUS 0.0 DAYS_DECISION 0.0 NAME_PAYMENT_TYPE 0.0 CODE_REJECT_REASON 0.0 NAME_TYPE_SUITE_y 0.0 NAME_CLIENT_TYPE 0.0 NAME_GOODS_CATEGORY 0.0 NAME_PORTFOLIO 0.0 NAME_PRODUCT_TYPE 0.0 CHANNEL_TYPE 0.0 SELLERPLACE_AREA 0.0 NAME_SELLER_INDUSTRY 0.0 CNT_PAYMENT 0.0 NAME_YIELD_GROUP 0.0 PRODUCT_COMBINATION 0.0 DAYS_FIRST_DRAWING 0.0 DAYS_FIRST_DUE 0.0 DAYS_LAST_DUE_1ST_VERSION 0.0 DAYS_LAST_DUE 0.0 DAYS_TERMINATION 0.0 NFLAG_INSURED_ON_APPROVAL 0.0 dtype: float64
mergeddf.isnull().sum()
TARGET 0 NAME_CONTRACT_TYPE_x 0 CODE_GENDER 0 FLAG_OWN_CAR 0 FLAG_OWN_REALTY 0 CNT_CHILDREN 0 AMT_INCOME_TOTAL 0 AMT_CREDIT_x 0 AMT_ANNUITY_x 0 AMT_GOODS_PRICE_x 0 NAME_TYPE_SUITE_x 0 NAME_INCOME_TYPE 0 NAME_EDUCATION_TYPE 0 NAME_FAMILY_STATUS 0 NAME_HOUSING_TYPE 0 REGION_POPULATION_RELATIVE 0 DAYS_BIRTH 0 DAYS_EMPLOYED 0 DAYS_REGISTRATION 0 DAYS_ID_PUBLISH 0 FLAG_MOBIL 0 FLAG_EMP_PHONE 0 FLAG_WORK_PHONE 0 FLAG_CONT_MOBILE 0 FLAG_PHONE 0 FLAG_EMAIL 0 CNT_FAM_MEMBERS 0 REGION_RATING_CLIENT 0 REGION_RATING_CLIENT_W_CITY 0 WEEKDAY_APPR_PROCESS_START_x 0 HOUR_APPR_PROCESS_START_x 0 REG_REGION_NOT_LIVE_REGION 0 REG_REGION_NOT_WORK_REGION 0 LIVE_REGION_NOT_WORK_REGION 0 REG_CITY_NOT_LIVE_CITY 0 REG_CITY_NOT_WORK_CITY 0 LIVE_CITY_NOT_WORK_CITY 0 ORGANIZATION_TYPE 0 OBS_30_CNT_SOCIAL_CIRCLE 0 DEF_30_CNT_SOCIAL_CIRCLE 0 OBS_60_CNT_SOCIAL_CIRCLE 0 DEF_60_CNT_SOCIAL_CIRCLE 0 DAYS_LAST_PHONE_CHANGE 0 FLAG_DOCUMENT_2 0 FLAG_DOCUMENT_3 0 FLAG_DOCUMENT_4 0 FLAG_DOCUMENT_5 0 FLAG_DOCUMENT_6 0 FLAG_DOCUMENT_7 0 FLAG_DOCUMENT_8 0 FLAG_DOCUMENT_9 0 FLAG_DOCUMENT_10 0 FLAG_DOCUMENT_11 0 FLAG_DOCUMENT_12 0 FLAG_DOCUMENT_13 0 FLAG_DOCUMENT_14 0 FLAG_DOCUMENT_15 0 FLAG_DOCUMENT_16 0 FLAG_DOCUMENT_17 0 FLAG_DOCUMENT_18 0 FLAG_DOCUMENT_19 0 FLAG_DOCUMENT_20 0 FLAG_DOCUMENT_21 0 AMT_REQ_CREDIT_BUREAU_HOUR 0 AMT_REQ_CREDIT_BUREAU_DAY 0 AMT_REQ_CREDIT_BUREAU_WEEK 0 AMT_REQ_CREDIT_BUREAU_MON 0 AMT_REQ_CREDIT_BUREAU_QRT 0 AMT_REQ_CREDIT_BUREAU_YEAR 0 SK_ID_PREV 0 NAME_CONTRACT_TYPE_y 0 AMT_ANNUITY_y 0 AMT_APPLICATION 0 AMT_CREDIT_y 0 AMT_GOODS_PRICE_y 0 WEEKDAY_APPR_PROCESS_START_y 0 HOUR_APPR_PROCESS_START_y 0 FLAG_LAST_APPL_PER_CONTRACT 0 NFLAG_LAST_APPL_IN_DAY 0 NAME_CASH_LOAN_PURPOSE 0 NAME_CONTRACT_STATUS 0 DAYS_DECISION 0 NAME_PAYMENT_TYPE 0 CODE_REJECT_REASON 0 NAME_TYPE_SUITE_y 0 NAME_CLIENT_TYPE 0 NAME_GOODS_CATEGORY 0 NAME_PORTFOLIO 0 NAME_PRODUCT_TYPE 0 CHANNEL_TYPE 0 SELLERPLACE_AREA 0 NAME_SELLER_INDUSTRY 0 CNT_PAYMENT 0 NAME_YIELD_GROUP 0 PRODUCT_COMBINATION 0 DAYS_FIRST_DRAWING 0 DAYS_FIRST_DUE 0 DAYS_LAST_DUE_1ST_VERSION 0 DAYS_LAST_DUE 0 DAYS_TERMINATION 0 NFLAG_INSURED_ON_APPROVAL 0 dtype: int64
mergeddf.head()
| TARGET | NAME_CONTRACT_TYPE_x | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT_x | AMT_ANNUITY_x | AMT_GOODS_PRICE_x | NAME_TYPE_SUITE_x | NAME_INCOME_TYPE | NAME_EDUCATION_TYPE | NAME_FAMILY_STATUS | NAME_HOUSING_TYPE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | WEEKDAY_APPR_PROCESS_START_x | HOUR_APPR_PROCESS_START_x | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | ORGANIZATION_TYPE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | SK_ID_PREV | NAME_CONTRACT_TYPE_y | AMT_ANNUITY_y | AMT_APPLICATION | AMT_CREDIT_y | AMT_GOODS_PRICE_y | WEEKDAY_APPR_PROCESS_START_y | HOUR_APPR_PROCESS_START_y | FLAG_LAST_APPL_PER_CONTRACT | NFLAG_LAST_APPL_IN_DAY | NAME_CASH_LOAN_PURPOSE | NAME_CONTRACT_STATUS | DAYS_DECISION | NAME_PAYMENT_TYPE | CODE_REJECT_REASON | NAME_TYPE_SUITE_y | NAME_CLIENT_TYPE | NAME_GOODS_CATEGORY | NAME_PORTFOLIO | NAME_PRODUCT_TYPE | CHANNEL_TYPE | SELLERPLACE_AREA | NAME_SELLER_INDUSTRY | CNT_PAYMENT | NAME_YIELD_GROUP | PRODUCT_COMBINATION | DAYS_FIRST_DRAWING | DAYS_FIRST_DUE | DAYS_LAST_DUE_1ST_VERSION | DAYS_LAST_DUE | DAYS_TERMINATION | NFLAG_INSURED_ON_APPROVAL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Cash loans | M | N | Y | 0 | 202500.0 | 406597.5 | 24700.5 | 351000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.018801 | 9461 | -637 | -3648.0 | 2120 | 1 | 1 | 0 | 1 | 1 | 0 | 1.0 | 2 | 2 | WEDNESDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 2.0 | 2.0 | 2.0 | 2.0 | 1134.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1038818 | Consumer loans | 9251.775 | 179055.0 | 179055.0 | 179055.0 | SATURDAY | 9 | Y | 1 | XAP | Approved | -606 | XNA | XAP | Unaccompanied | New | Vehicles | POS | XNA | Stone | 500 | Auto technology | 24.0 | low_normal | POS other with interest | 365243.0 | -565.0 | 125.0 | -25.0 | -17.0 | 0.0 |
| 1 | 0 | Cash loans | F | N | N | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1810518 | Cash loans | 98356.995 | 900000.0 | 1035882.0 | 900000.0 | FRIDAY | 12 | Y | 1 | XNA | Approved | -746 | XNA | XAP | Unaccompanied | Repeater | XNA | Cash | x-sell | Credit and cash offices | -1 | XNA | 12.0 | low_normal | Cash X-Sell: low | 365243.0 | -716.0 | -386.0 | -536.0 | -527.0 | 1.0 |
| 2 | 0 | Cash loans | F | N | N | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2636178 | Consumer loans | 64567.665 | 337500.0 | 348637.5 | 337500.0 | SUNDAY | 17 | Y | 1 | XAP | Approved | -828 | Cash through the bank | XAP | Family | Refreshed | Furniture | POS | XNA | Stone | 1400 | Furniture | 6.0 | middle | POS industry with interest | 365243.0 | -797.0 | -647.0 | -647.0 | -639.0 | 0.0 |
| 3 | 0 | Cash loans | F | N | N | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2396755 | Consumer loans | 6737.310 | 68809.5 | 68053.5 | 68809.5 | SATURDAY | 15 | Y | 1 | XAP | Approved | -2341 | Cash through the bank | XAP | Family | Refreshed | Consumer Electronics | POS | XNA | Country-wide | 200 | Consumer electronics | 12.0 | middle | POS household with interest | 365243.0 | -2310.0 | -1980.0 | -1980.0 | -1976.0 | 1.0 |
| 4 | 0 | Revolving loans | M | Y | Y | 0 | 67500.0 | 135000.0 | 6750.0 | 135000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.010032 | 19046 | -225 | -4260.0 | 2531 | 1 | 1 | 1 | 1 | 1 | 0 | 1.0 | 2 | 2 | MONDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Government | 0.0 | 0.0 | 0.0 | 0.0 | 815.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1564014 | Consumer loans | 5357.250 | 24282.0 | 20106.0 | 24282.0 | FRIDAY | 5 | Y | 1 | XAP | Approved | -815 | Cash through the bank | XAP | Unaccompanied | New | Mobile | POS | XNA | Regional / Local | 30 | Connectivity | 4.0 | middle | POS mobile without interest | 365243.0 | -784.0 | -694.0 | -724.0 | -714.0 | 0.0 |
# List of variables to map
varlist = ['FLAG_OWN_CAR','FLAG_OWN_REALTY','FLAG_LAST_APPL_PER_CONTRACT']
# Defining the map function
def binary_map(x):
return x.map({'Y': 1, "N": 0})
# Applying the function to the housing list
mergeddf[varlist] = mergeddf[varlist].apply(binary_map)
mergeddf.head()
| TARGET | NAME_CONTRACT_TYPE_x | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT_x | AMT_ANNUITY_x | AMT_GOODS_PRICE_x | NAME_TYPE_SUITE_x | NAME_INCOME_TYPE | NAME_EDUCATION_TYPE | NAME_FAMILY_STATUS | NAME_HOUSING_TYPE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | WEEKDAY_APPR_PROCESS_START_x | HOUR_APPR_PROCESS_START_x | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | ORGANIZATION_TYPE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | SK_ID_PREV | NAME_CONTRACT_TYPE_y | AMT_ANNUITY_y | AMT_APPLICATION | AMT_CREDIT_y | AMT_GOODS_PRICE_y | WEEKDAY_APPR_PROCESS_START_y | HOUR_APPR_PROCESS_START_y | FLAG_LAST_APPL_PER_CONTRACT | NFLAG_LAST_APPL_IN_DAY | NAME_CASH_LOAN_PURPOSE | NAME_CONTRACT_STATUS | DAYS_DECISION | NAME_PAYMENT_TYPE | CODE_REJECT_REASON | NAME_TYPE_SUITE_y | NAME_CLIENT_TYPE | NAME_GOODS_CATEGORY | NAME_PORTFOLIO | NAME_PRODUCT_TYPE | CHANNEL_TYPE | SELLERPLACE_AREA | NAME_SELLER_INDUSTRY | CNT_PAYMENT | NAME_YIELD_GROUP | PRODUCT_COMBINATION | DAYS_FIRST_DRAWING | DAYS_FIRST_DUE | DAYS_LAST_DUE_1ST_VERSION | DAYS_LAST_DUE | DAYS_TERMINATION | NFLAG_INSURED_ON_APPROVAL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Cash loans | M | 0 | 1 | 0 | 202500.0 | 406597.5 | 24700.5 | 351000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.018801 | 9461 | -637 | -3648.0 | 2120 | 1 | 1 | 0 | 1 | 1 | 0 | 1.0 | 2 | 2 | WEDNESDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 2.0 | 2.0 | 2.0 | 2.0 | 1134.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1038818 | Consumer loans | 9251.775 | 179055.0 | 179055.0 | 179055.0 | SATURDAY | 9 | 1 | 1 | XAP | Approved | -606 | XNA | XAP | Unaccompanied | New | Vehicles | POS | XNA | Stone | 500 | Auto technology | 24.0 | low_normal | POS other with interest | 365243.0 | -565.0 | 125.0 | -25.0 | -17.0 | 0.0 |
| 1 | 0 | Cash loans | F | 0 | 0 | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1810518 | Cash loans | 98356.995 | 900000.0 | 1035882.0 | 900000.0 | FRIDAY | 12 | 1 | 1 | XNA | Approved | -746 | XNA | XAP | Unaccompanied | Repeater | XNA | Cash | x-sell | Credit and cash offices | -1 | XNA | 12.0 | low_normal | Cash X-Sell: low | 365243.0 | -716.0 | -386.0 | -536.0 | -527.0 | 1.0 |
| 2 | 0 | Cash loans | F | 0 | 0 | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2636178 | Consumer loans | 64567.665 | 337500.0 | 348637.5 | 337500.0 | SUNDAY | 17 | 1 | 1 | XAP | Approved | -828 | Cash through the bank | XAP | Family | Refreshed | Furniture | POS | XNA | Stone | 1400 | Furniture | 6.0 | middle | POS industry with interest | 365243.0 | -797.0 | -647.0 | -647.0 | -639.0 | 0.0 |
| 3 | 0 | Cash loans | F | 0 | 0 | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2396755 | Consumer loans | 6737.310 | 68809.5 | 68053.5 | 68809.5 | SATURDAY | 15 | 1 | 1 | XAP | Approved | -2341 | Cash through the bank | XAP | Family | Refreshed | Consumer Electronics | POS | XNA | Country-wide | 200 | Consumer electronics | 12.0 | middle | POS household with interest | 365243.0 | -2310.0 | -1980.0 | -1980.0 | -1976.0 | 1.0 |
| 4 | 0 | Revolving loans | M | 1 | 1 | 0 | 67500.0 | 135000.0 | 6750.0 | 135000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.010032 | 19046 | -225 | -4260.0 | 2531 | 1 | 1 | 1 | 1 | 1 | 0 | 1.0 | 2 | 2 | MONDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Government | 0.0 | 0.0 | 0.0 | 0.0 | 815.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1564014 | Consumer loans | 5357.250 | 24282.0 | 20106.0 | 24282.0 | FRIDAY | 5 | 1 | 1 | XAP | Approved | -815 | Cash through the bank | XAP | Unaccompanied | New | Mobile | POS | XNA | Regional / Local | 30 | Connectivity | 4.0 | middle | POS mobile without interest | 365243.0 | -784.0 | -694.0 | -724.0 | -714.0 | 0.0 |
mergeddf.drop(['SK_ID_PREV'], 1, inplace = True)
mergeddf.head()
| TARGET | NAME_CONTRACT_TYPE_x | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT_x | AMT_ANNUITY_x | AMT_GOODS_PRICE_x | NAME_TYPE_SUITE_x | NAME_INCOME_TYPE | NAME_EDUCATION_TYPE | NAME_FAMILY_STATUS | NAME_HOUSING_TYPE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | WEEKDAY_APPR_PROCESS_START_x | HOUR_APPR_PROCESS_START_x | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | ORGANIZATION_TYPE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | NAME_CONTRACT_TYPE_y | AMT_ANNUITY_y | AMT_APPLICATION | AMT_CREDIT_y | AMT_GOODS_PRICE_y | WEEKDAY_APPR_PROCESS_START_y | HOUR_APPR_PROCESS_START_y | FLAG_LAST_APPL_PER_CONTRACT | NFLAG_LAST_APPL_IN_DAY | NAME_CASH_LOAN_PURPOSE | NAME_CONTRACT_STATUS | DAYS_DECISION | NAME_PAYMENT_TYPE | CODE_REJECT_REASON | NAME_TYPE_SUITE_y | NAME_CLIENT_TYPE | NAME_GOODS_CATEGORY | NAME_PORTFOLIO | NAME_PRODUCT_TYPE | CHANNEL_TYPE | SELLERPLACE_AREA | NAME_SELLER_INDUSTRY | CNT_PAYMENT | NAME_YIELD_GROUP | PRODUCT_COMBINATION | DAYS_FIRST_DRAWING | DAYS_FIRST_DUE | DAYS_LAST_DUE_1ST_VERSION | DAYS_LAST_DUE | DAYS_TERMINATION | NFLAG_INSURED_ON_APPROVAL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Cash loans | M | 0 | 1 | 0 | 202500.0 | 406597.5 | 24700.5 | 351000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.018801 | 9461 | -637 | -3648.0 | 2120 | 1 | 1 | 0 | 1 | 1 | 0 | 1.0 | 2 | 2 | WEDNESDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 2.0 | 2.0 | 2.0 | 2.0 | 1134.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | Consumer loans | 9251.775 | 179055.0 | 179055.0 | 179055.0 | SATURDAY | 9 | 1 | 1 | XAP | Approved | -606 | XNA | XAP | Unaccompanied | New | Vehicles | POS | XNA | Stone | 500 | Auto technology | 24.0 | low_normal | POS other with interest | 365243.0 | -565.0 | 125.0 | -25.0 | -17.0 | 0.0 |
| 1 | 0 | Cash loans | F | 0 | 0 | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | Cash loans | 98356.995 | 900000.0 | 1035882.0 | 900000.0 | FRIDAY | 12 | 1 | 1 | XNA | Approved | -746 | XNA | XAP | Unaccompanied | Repeater | XNA | Cash | x-sell | Credit and cash offices | -1 | XNA | 12.0 | low_normal | Cash X-Sell: low | 365243.0 | -716.0 | -386.0 | -536.0 | -527.0 | 1.0 |
| 2 | 0 | Cash loans | F | 0 | 0 | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | Consumer loans | 64567.665 | 337500.0 | 348637.5 | 337500.0 | SUNDAY | 17 | 1 | 1 | XAP | Approved | -828 | Cash through the bank | XAP | Family | Refreshed | Furniture | POS | XNA | Stone | 1400 | Furniture | 6.0 | middle | POS industry with interest | 365243.0 | -797.0 | -647.0 | -647.0 | -639.0 | 0.0 |
| 3 | 0 | Cash loans | F | 0 | 0 | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | Consumer loans | 6737.310 | 68809.5 | 68053.5 | 68809.5 | SATURDAY | 15 | 1 | 1 | XAP | Approved | -2341 | Cash through the bank | XAP | Family | Refreshed | Consumer Electronics | POS | XNA | Country-wide | 200 | Consumer electronics | 12.0 | middle | POS household with interest | 365243.0 | -2310.0 | -1980.0 | -1980.0 | -1976.0 | 1.0 |
| 4 | 0 | Revolving loans | M | 1 | 1 | 0 | 67500.0 | 135000.0 | 6750.0 | 135000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.010032 | 19046 | -225 | -4260.0 | 2531 | 1 | 1 | 1 | 1 | 1 | 0 | 1.0 | 2 | 2 | MONDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Government | 0.0 | 0.0 | 0.0 | 0.0 | 815.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | Consumer loans | 5357.250 | 24282.0 | 20106.0 | 24282.0 | FRIDAY | 5 | 1 | 1 | XAP | Approved | -815 | Cash through the bank | XAP | Unaccompanied | New | Mobile | POS | XNA | Regional / Local | 30 | Connectivity | 4.0 | middle | POS mobile without interest | 365243.0 | -784.0 | -694.0 | -724.0 | -714.0 | 0.0 |
mergeddf[['FLAG_OWN_CAR','FLAG_OWN_REALTY','FLAG_MOBIL',
'FLAG_EMP_PHONE','FLAG_WORK_PHONE','FLAG_CONT_MOBILE',
'FLAG_PHONE','FLAG_EMAIL','REGION_RATING_CLIENT',
'REGION_RATING_CLIENT_W_CITY','REG_REGION_NOT_LIVE_REGION',
'REG_REGION_NOT_WORK_REGION','LIVE_REGION_NOT_WORK_REGION',
'REG_CITY_NOT_LIVE_CITY','REG_CITY_NOT_WORK_CITY','FLAG_DOCUMENT_2',
'FLAG_DOCUMENT_3','FLAG_DOCUMENT_4','FLAG_DOCUMENT_5','FLAG_DOCUMENT_6',
'FLAG_DOCUMENT_7','FLAG_DOCUMENT_8','FLAG_DOCUMENT_9',
'FLAG_DOCUMENT_10','FLAG_DOCUMENT_11','FLAG_DOCUMENT_12','FLAG_DOCUMENT_13',
'FLAG_DOCUMENT_14','FLAG_DOCUMENT_15','FLAG_DOCUMENT_16','FLAG_DOCUMENT_17',
'FLAG_DOCUMENT_18','FLAG_DOCUMENT_19','FLAG_DOCUMENT_20','FLAG_DOCUMENT_21',
'NFLAG_INSURED_ON_APPROVAL']]= mergeddf[['FLAG_OWN_CAR','FLAG_OWN_REALTY','FLAG_MOBIL',
'FLAG_EMP_PHONE','FLAG_WORK_PHONE','FLAG_CONT_MOBILE',
'FLAG_PHONE','FLAG_EMAIL','REGION_RATING_CLIENT',
'REGION_RATING_CLIENT_W_CITY','REG_REGION_NOT_LIVE_REGION',
'REG_REGION_NOT_WORK_REGION','LIVE_REGION_NOT_WORK_REGION',
'REG_CITY_NOT_LIVE_CITY','REG_CITY_NOT_WORK_CITY','FLAG_DOCUMENT_2',
'FLAG_DOCUMENT_3','FLAG_DOCUMENT_4','FLAG_DOCUMENT_5','FLAG_DOCUMENT_6',
'FLAG_DOCUMENT_7','FLAG_DOCUMENT_8','FLAG_DOCUMENT_9',
'FLAG_DOCUMENT_10','FLAG_DOCUMENT_11','FLAG_DOCUMENT_12','FLAG_DOCUMENT_13',
'FLAG_DOCUMENT_14','FLAG_DOCUMENT_15','FLAG_DOCUMENT_16','FLAG_DOCUMENT_17',
'FLAG_DOCUMENT_18','FLAG_DOCUMENT_19','FLAG_DOCUMENT_20','FLAG_DOCUMENT_21',
'NFLAG_INSURED_ON_APPROVAL']].astype('category')
obj_dtypes = [i for i in mergeddf.select_dtypes(include=object).columns if i not in ["type"] ]
num_dtypes = [i for i in mergeddf.select_dtypes(include = np.number).columns if i not in [ 'TARGET']]
num_dtypes
['CNT_CHILDREN', 'AMT_INCOME_TOTAL', 'AMT_CREDIT_x', 'AMT_ANNUITY_x', 'AMT_GOODS_PRICE_x', 'REGION_POPULATION_RELATIVE', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'DAYS_REGISTRATION', 'DAYS_ID_PUBLISH', 'CNT_FAM_MEMBERS', 'HOUR_APPR_PROCESS_START_x', 'LIVE_CITY_NOT_WORK_CITY', 'OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_SOCIAL_CIRCLE', 'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE', 'DAYS_LAST_PHONE_CHANGE', 'AMT_REQ_CREDIT_BUREAU_HOUR', 'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR', 'AMT_ANNUITY_y', 'AMT_APPLICATION', 'AMT_CREDIT_y', 'AMT_GOODS_PRICE_y', 'HOUR_APPR_PROCESS_START_y', 'FLAG_LAST_APPL_PER_CONTRACT', 'NFLAG_LAST_APPL_IN_DAY', 'DAYS_DECISION', 'SELLERPLACE_AREA', 'CNT_PAYMENT', 'DAYS_FIRST_DRAWING', 'DAYS_FIRST_DUE', 'DAYS_LAST_DUE_1ST_VERSION', 'DAYS_LAST_DUE', 'DAYS_TERMINATION']
obj_dtypes
['NAME_CONTRACT_TYPE_x', 'CODE_GENDER', 'NAME_TYPE_SUITE_x', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'WEEKDAY_APPR_PROCESS_START_x', 'ORGANIZATION_TYPE', 'NAME_CONTRACT_TYPE_y', 'WEEKDAY_APPR_PROCESS_START_y', 'NAME_CASH_LOAN_PURPOSE', 'NAME_CONTRACT_STATUS', 'NAME_PAYMENT_TYPE', 'CODE_REJECT_REASON', 'NAME_TYPE_SUITE_y', 'NAME_CLIENT_TYPE', 'NAME_GOODS_CATEGORY', 'NAME_PORTFOLIO', 'NAME_PRODUCT_TYPE', 'CHANNEL_TYPE', 'NAME_SELLER_INDUSTRY', 'NAME_YIELD_GROUP', 'PRODUCT_COMBINATION']
# Creating a dummy variable for some of the categorical variables and dropping the first one.
dummy1 = pd.get_dummies(mergeddf[['FLAG_OWN_CAR','FLAG_OWN_REALTY','FLAG_MOBIL',
'FLAG_EMP_PHONE','FLAG_WORK_PHONE','FLAG_CONT_MOBILE',
'FLAG_PHONE','FLAG_EMAIL','REGION_RATING_CLIENT',
'REGION_RATING_CLIENT_W_CITY','REG_REGION_NOT_LIVE_REGION',
'REG_REGION_NOT_WORK_REGION','LIVE_REGION_NOT_WORK_REGION',
'REG_CITY_NOT_LIVE_CITY','REG_CITY_NOT_WORK_CITY','FLAG_DOCUMENT_2',
'FLAG_DOCUMENT_3','FLAG_DOCUMENT_4','FLAG_DOCUMENT_5','FLAG_DOCUMENT_6',
'FLAG_DOCUMENT_7','FLAG_DOCUMENT_8','FLAG_DOCUMENT_9',
'FLAG_DOCUMENT_10','FLAG_DOCUMENT_11','FLAG_DOCUMENT_12','FLAG_DOCUMENT_13',
'FLAG_DOCUMENT_14','FLAG_DOCUMENT_15','FLAG_DOCUMENT_16','FLAG_DOCUMENT_17',
'FLAG_DOCUMENT_18','FLAG_DOCUMENT_19','FLAG_DOCUMENT_20','FLAG_DOCUMENT_21',
'NFLAG_INSURED_ON_APPROVAL','NAME_CONTRACT_TYPE_x','CODE_GENDER',
'NAME_TYPE_SUITE_x','NAME_INCOME_TYPE','NAME_EDUCATION_TYPE','NAME_FAMILY_STATUS',
'NAME_HOUSING_TYPE','WEEKDAY_APPR_PROCESS_START_x','ORGANIZATION_TYPE','NAME_CONTRACT_TYPE_y',
'WEEKDAY_APPR_PROCESS_START_y','NAME_CASH_LOAN_PURPOSE','NAME_CONTRACT_STATUS','NAME_PAYMENT_TYPE',
'CODE_REJECT_REASON','NAME_TYPE_SUITE_y','NAME_CLIENT_TYPE','NAME_GOODS_CATEGORY',
'NAME_PORTFOLIO','NAME_PRODUCT_TYPE','CHANNEL_TYPE','NAME_SELLER_INDUSTRY',
'NAME_YIELD_GROUP','PRODUCT_COMBINATION']], drop_first=True)
dummy1.head()
| FLAG_OWN_CAR_1 | FLAG_OWN_REALTY_1 | FLAG_EMP_PHONE_1 | FLAG_WORK_PHONE_1 | FLAG_CONT_MOBILE_1 | FLAG_PHONE_1 | FLAG_EMAIL_1 | REGION_RATING_CLIENT_2 | REGION_RATING_CLIENT_3 | REGION_RATING_CLIENT_W_CITY_2 | REGION_RATING_CLIENT_W_CITY_3 | REG_REGION_NOT_LIVE_REGION_1 | REG_REGION_NOT_WORK_REGION_1 | LIVE_REGION_NOT_WORK_REGION_1 | REG_CITY_NOT_LIVE_CITY_1 | REG_CITY_NOT_WORK_CITY_1 | FLAG_DOCUMENT_2_1 | FLAG_DOCUMENT_3_1 | FLAG_DOCUMENT_4_1 | FLAG_DOCUMENT_5_1 | FLAG_DOCUMENT_6_1 | FLAG_DOCUMENT_7_1 | FLAG_DOCUMENT_8_1 | FLAG_DOCUMENT_9_1 | FLAG_DOCUMENT_10_1 | FLAG_DOCUMENT_11_1 | FLAG_DOCUMENT_12_1 | FLAG_DOCUMENT_13_1 | FLAG_DOCUMENT_14_1 | FLAG_DOCUMENT_15_1 | FLAG_DOCUMENT_16_1 | FLAG_DOCUMENT_17_1 | FLAG_DOCUMENT_18_1 | FLAG_DOCUMENT_19_1 | FLAG_DOCUMENT_20_1 | FLAG_DOCUMENT_21_1 | NFLAG_INSURED_ON_APPROVAL_1.0 | NAME_CONTRACT_TYPE_x_Revolving loans | CODE_GENDER_M | CODE_GENDER_XNA | NAME_TYPE_SUITE_x_Family | NAME_TYPE_SUITE_x_Group of people | NAME_TYPE_SUITE_x_Other_A | NAME_TYPE_SUITE_x_Other_B | NAME_TYPE_SUITE_x_Spouse, partner | NAME_TYPE_SUITE_x_Unaccompanied | NAME_INCOME_TYPE_Maternity leave | NAME_INCOME_TYPE_Pensioner | NAME_INCOME_TYPE_State servant | NAME_INCOME_TYPE_Student | NAME_INCOME_TYPE_Unemployed | NAME_INCOME_TYPE_Working | NAME_EDUCATION_TYPE_Higher education | NAME_EDUCATION_TYPE_Incomplete higher | NAME_EDUCATION_TYPE_Lower secondary | NAME_EDUCATION_TYPE_Secondary / secondary special | NAME_FAMILY_STATUS_Married | NAME_FAMILY_STATUS_Separated | NAME_FAMILY_STATUS_Single / not married | NAME_FAMILY_STATUS_Widow | NAME_HOUSING_TYPE_House / apartment | NAME_HOUSING_TYPE_Municipal apartment | NAME_HOUSING_TYPE_Office apartment | NAME_HOUSING_TYPE_Rented apartment | NAME_HOUSING_TYPE_With parents | WEEKDAY_APPR_PROCESS_START_x_MONDAY | WEEKDAY_APPR_PROCESS_START_x_SATURDAY | WEEKDAY_APPR_PROCESS_START_x_SUNDAY | WEEKDAY_APPR_PROCESS_START_x_THURSDAY | WEEKDAY_APPR_PROCESS_START_x_TUESDAY | WEEKDAY_APPR_PROCESS_START_x_WEDNESDAY | ORGANIZATION_TYPE_Agriculture | ORGANIZATION_TYPE_Bank | ORGANIZATION_TYPE_Business Entity Type 1 | ORGANIZATION_TYPE_Business Entity Type 2 | ORGANIZATION_TYPE_Business Entity Type 3 | ORGANIZATION_TYPE_Cleaning | ORGANIZATION_TYPE_Construction | ORGANIZATION_TYPE_Culture | ORGANIZATION_TYPE_Electricity | ORGANIZATION_TYPE_Emergency | ORGANIZATION_TYPE_Government | ORGANIZATION_TYPE_Hotel | ORGANIZATION_TYPE_Housing | ORGANIZATION_TYPE_Industry: type 1 | ORGANIZATION_TYPE_Industry: type 10 | ORGANIZATION_TYPE_Industry: type 11 | ORGANIZATION_TYPE_Industry: type 12 | ORGANIZATION_TYPE_Industry: type 13 | ORGANIZATION_TYPE_Industry: type 2 | ORGANIZATION_TYPE_Industry: type 3 | ORGANIZATION_TYPE_Industry: type 4 | ORGANIZATION_TYPE_Industry: type 5 | ORGANIZATION_TYPE_Industry: type 6 | ORGANIZATION_TYPE_Industry: type 7 | ORGANIZATION_TYPE_Industry: type 8 | ORGANIZATION_TYPE_Industry: type 9 | ORGANIZATION_TYPE_Insurance | ORGANIZATION_TYPE_Kindergarten | ORGANIZATION_TYPE_Legal Services | ORGANIZATION_TYPE_Medicine | ORGANIZATION_TYPE_Military | ORGANIZATION_TYPE_Mobile | ORGANIZATION_TYPE_Other | ORGANIZATION_TYPE_Police | ORGANIZATION_TYPE_Postal | ORGANIZATION_TYPE_Realtor | ORGANIZATION_TYPE_Religion | ORGANIZATION_TYPE_Restaurant | ORGANIZATION_TYPE_School | ORGANIZATION_TYPE_Security | ORGANIZATION_TYPE_Security Ministries | ORGANIZATION_TYPE_Self-employed | ORGANIZATION_TYPE_Services | ORGANIZATION_TYPE_Telecom | ORGANIZATION_TYPE_Trade: type 1 | ORGANIZATION_TYPE_Trade: type 2 | ORGANIZATION_TYPE_Trade: type 3 | ORGANIZATION_TYPE_Trade: type 4 | ORGANIZATION_TYPE_Trade: type 5 | ORGANIZATION_TYPE_Trade: type 6 | ORGANIZATION_TYPE_Trade: type 7 | ORGANIZATION_TYPE_Transport: type 1 | ORGANIZATION_TYPE_Transport: type 2 | ORGANIZATION_TYPE_Transport: type 3 | ORGANIZATION_TYPE_Transport: type 4 | ORGANIZATION_TYPE_University | ORGANIZATION_TYPE_XNA | NAME_CONTRACT_TYPE_y_Consumer loans | NAME_CONTRACT_TYPE_y_Revolving loans | WEEKDAY_APPR_PROCESS_START_y_MONDAY | WEEKDAY_APPR_PROCESS_START_y_SATURDAY | WEEKDAY_APPR_PROCESS_START_y_SUNDAY | WEEKDAY_APPR_PROCESS_START_y_THURSDAY | WEEKDAY_APPR_PROCESS_START_y_TUESDAY | WEEKDAY_APPR_PROCESS_START_y_WEDNESDAY | NAME_CASH_LOAN_PURPOSE_Business development | NAME_CASH_LOAN_PURPOSE_Buying a garage | NAME_CASH_LOAN_PURPOSE_Buying a holiday home / land | NAME_CASH_LOAN_PURPOSE_Buying a home | NAME_CASH_LOAN_PURPOSE_Buying a new car | NAME_CASH_LOAN_PURPOSE_Buying a used car | NAME_CASH_LOAN_PURPOSE_Car repairs | NAME_CASH_LOAN_PURPOSE_Education | NAME_CASH_LOAN_PURPOSE_Everyday expenses | NAME_CASH_LOAN_PURPOSE_Furniture | NAME_CASH_LOAN_PURPOSE_Gasification / water supply | NAME_CASH_LOAN_PURPOSE_Hobby | NAME_CASH_LOAN_PURPOSE_Journey | NAME_CASH_LOAN_PURPOSE_Medicine | NAME_CASH_LOAN_PURPOSE_Money for a third person | NAME_CASH_LOAN_PURPOSE_Other | NAME_CASH_LOAN_PURPOSE_Payments on other loans | NAME_CASH_LOAN_PURPOSE_Purchase of electronic equipment | NAME_CASH_LOAN_PURPOSE_Refusal to name the goal | NAME_CASH_LOAN_PURPOSE_Repairs | NAME_CASH_LOAN_PURPOSE_Urgent needs | NAME_CASH_LOAN_PURPOSE_Wedding / gift / holiday | NAME_CASH_LOAN_PURPOSE_XAP | NAME_CASH_LOAN_PURPOSE_XNA | NAME_CONTRACT_STATUS_Canceled | NAME_CONTRACT_STATUS_Refused | NAME_CONTRACT_STATUS_Unused offer | NAME_PAYMENT_TYPE_Cashless from the account of the employer | NAME_PAYMENT_TYPE_Non-cash from your account | NAME_PAYMENT_TYPE_XNA | CODE_REJECT_REASON_HC | CODE_REJECT_REASON_LIMIT | CODE_REJECT_REASON_SCO | CODE_REJECT_REASON_SCOFR | CODE_REJECT_REASON_SYSTEM | CODE_REJECT_REASON_VERIF | CODE_REJECT_REASON_XAP | CODE_REJECT_REASON_XNA | NAME_TYPE_SUITE_y_Family | NAME_TYPE_SUITE_y_Group of people | NAME_TYPE_SUITE_y_Other_A | NAME_TYPE_SUITE_y_Other_B | NAME_TYPE_SUITE_y_Spouse, partner | NAME_TYPE_SUITE_y_Unaccompanied | NAME_CLIENT_TYPE_Refreshed | NAME_CLIENT_TYPE_Repeater | NAME_CLIENT_TYPE_XNA | NAME_GOODS_CATEGORY_Animals | NAME_GOODS_CATEGORY_Audio/Video | NAME_GOODS_CATEGORY_Auto Accessories | NAME_GOODS_CATEGORY_Clothing and Accessories | NAME_GOODS_CATEGORY_Computers | NAME_GOODS_CATEGORY_Construction Materials | NAME_GOODS_CATEGORY_Consumer Electronics | NAME_GOODS_CATEGORY_Direct Sales | NAME_GOODS_CATEGORY_Education | NAME_GOODS_CATEGORY_Fitness | NAME_GOODS_CATEGORY_Furniture | NAME_GOODS_CATEGORY_Gardening | NAME_GOODS_CATEGORY_Homewares | NAME_GOODS_CATEGORY_Insurance | NAME_GOODS_CATEGORY_Jewelry | NAME_GOODS_CATEGORY_Medical Supplies | NAME_GOODS_CATEGORY_Medicine | NAME_GOODS_CATEGORY_Mobile | NAME_GOODS_CATEGORY_Office Appliances | NAME_GOODS_CATEGORY_Other | NAME_GOODS_CATEGORY_Photo / Cinema Equipment | NAME_GOODS_CATEGORY_Sport and Leisure | NAME_GOODS_CATEGORY_Tourism | NAME_GOODS_CATEGORY_Vehicles | NAME_GOODS_CATEGORY_Weapon | NAME_GOODS_CATEGORY_XNA | NAME_PORTFOLIO_Cars | NAME_PORTFOLIO_Cash | NAME_PORTFOLIO_POS | NAME_PORTFOLIO_XNA | NAME_PRODUCT_TYPE_walk-in | NAME_PRODUCT_TYPE_x-sell | CHANNEL_TYPE_Car dealer | CHANNEL_TYPE_Channel of corporate sales | CHANNEL_TYPE_Contact center | CHANNEL_TYPE_Country-wide | CHANNEL_TYPE_Credit and cash offices | CHANNEL_TYPE_Regional / Local | CHANNEL_TYPE_Stone | NAME_SELLER_INDUSTRY_Clothing | NAME_SELLER_INDUSTRY_Connectivity | NAME_SELLER_INDUSTRY_Construction | NAME_SELLER_INDUSTRY_Consumer electronics | NAME_SELLER_INDUSTRY_Furniture | NAME_SELLER_INDUSTRY_Industry | NAME_SELLER_INDUSTRY_Jewelry | NAME_SELLER_INDUSTRY_MLM partners | NAME_SELLER_INDUSTRY_Tourism | NAME_SELLER_INDUSTRY_XNA | NAME_YIELD_GROUP_high | NAME_YIELD_GROUP_low_action | NAME_YIELD_GROUP_low_normal | NAME_YIELD_GROUP_middle | PRODUCT_COMBINATION_Card X-Sell | PRODUCT_COMBINATION_Cash | PRODUCT_COMBINATION_Cash Street: high | PRODUCT_COMBINATION_Cash Street: low | PRODUCT_COMBINATION_Cash Street: middle | PRODUCT_COMBINATION_Cash X-Sell: high | PRODUCT_COMBINATION_Cash X-Sell: low | PRODUCT_COMBINATION_Cash X-Sell: middle | PRODUCT_COMBINATION_POS household with interest | PRODUCT_COMBINATION_POS household without interest | PRODUCT_COMBINATION_POS industry with interest | PRODUCT_COMBINATION_POS industry without interest | PRODUCT_COMBINATION_POS mobile with interest | PRODUCT_COMBINATION_POS mobile without interest | PRODUCT_COMBINATION_POS other with interest | PRODUCT_COMBINATION_POS others without interest | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 3 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
# Adding the results to the master dataframe
mergeddf = pd.concat([mergeddf, dummy1], axis=1)
mergeddf.head()
| TARGET | NAME_CONTRACT_TYPE_x | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT_x | AMT_ANNUITY_x | AMT_GOODS_PRICE_x | NAME_TYPE_SUITE_x | NAME_INCOME_TYPE | NAME_EDUCATION_TYPE | NAME_FAMILY_STATUS | NAME_HOUSING_TYPE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | WEEKDAY_APPR_PROCESS_START_x | HOUR_APPR_PROCESS_START_x | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | ORGANIZATION_TYPE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | NAME_CONTRACT_TYPE_y | AMT_ANNUITY_y | AMT_APPLICATION | AMT_CREDIT_y | AMT_GOODS_PRICE_y | WEEKDAY_APPR_PROCESS_START_y | HOUR_APPR_PROCESS_START_y | FLAG_LAST_APPL_PER_CONTRACT | NFLAG_LAST_APPL_IN_DAY | NAME_CASH_LOAN_PURPOSE | NAME_CONTRACT_STATUS | DAYS_DECISION | NAME_PAYMENT_TYPE | CODE_REJECT_REASON | NAME_TYPE_SUITE_y | NAME_CLIENT_TYPE | NAME_GOODS_CATEGORY | NAME_PORTFOLIO | NAME_PRODUCT_TYPE | CHANNEL_TYPE | SELLERPLACE_AREA | NAME_SELLER_INDUSTRY | CNT_PAYMENT | NAME_YIELD_GROUP | PRODUCT_COMBINATION | DAYS_FIRST_DRAWING | DAYS_FIRST_DUE | DAYS_LAST_DUE_1ST_VERSION | DAYS_LAST_DUE | DAYS_TERMINATION | NFLAG_INSURED_ON_APPROVAL | FLAG_OWN_CAR_1 | FLAG_OWN_REALTY_1 | FLAG_EMP_PHONE_1 | FLAG_WORK_PHONE_1 | FLAG_CONT_MOBILE_1 | FLAG_PHONE_1 | FLAG_EMAIL_1 | REGION_RATING_CLIENT_2 | REGION_RATING_CLIENT_3 | REGION_RATING_CLIENT_W_CITY_2 | REGION_RATING_CLIENT_W_CITY_3 | REG_REGION_NOT_LIVE_REGION_1 | REG_REGION_NOT_WORK_REGION_1 | LIVE_REGION_NOT_WORK_REGION_1 | REG_CITY_NOT_LIVE_CITY_1 | REG_CITY_NOT_WORK_CITY_1 | FLAG_DOCUMENT_2_1 | FLAG_DOCUMENT_3_1 | FLAG_DOCUMENT_4_1 | FLAG_DOCUMENT_5_1 | FLAG_DOCUMENT_6_1 | FLAG_DOCUMENT_7_1 | FLAG_DOCUMENT_8_1 | FLAG_DOCUMENT_9_1 | FLAG_DOCUMENT_10_1 | FLAG_DOCUMENT_11_1 | FLAG_DOCUMENT_12_1 | FLAG_DOCUMENT_13_1 | FLAG_DOCUMENT_14_1 | FLAG_DOCUMENT_15_1 | FLAG_DOCUMENT_16_1 | FLAG_DOCUMENT_17_1 | FLAG_DOCUMENT_18_1 | FLAG_DOCUMENT_19_1 | FLAG_DOCUMENT_20_1 | FLAG_DOCUMENT_21_1 | NFLAG_INSURED_ON_APPROVAL_1.0 | NAME_CONTRACT_TYPE_x_Revolving loans | CODE_GENDER_M | CODE_GENDER_XNA | NAME_TYPE_SUITE_x_Family | NAME_TYPE_SUITE_x_Group of people | NAME_TYPE_SUITE_x_Other_A | NAME_TYPE_SUITE_x_Other_B | NAME_TYPE_SUITE_x_Spouse, partner | NAME_TYPE_SUITE_x_Unaccompanied | NAME_INCOME_TYPE_Maternity leave | NAME_INCOME_TYPE_Pensioner | NAME_INCOME_TYPE_State servant | NAME_INCOME_TYPE_Student | NAME_INCOME_TYPE_Unemployed | NAME_INCOME_TYPE_Working | NAME_EDUCATION_TYPE_Higher education | NAME_EDUCATION_TYPE_Incomplete higher | NAME_EDUCATION_TYPE_Lower secondary | NAME_EDUCATION_TYPE_Secondary / secondary special | NAME_FAMILY_STATUS_Married | NAME_FAMILY_STATUS_Separated | NAME_FAMILY_STATUS_Single / not married | NAME_FAMILY_STATUS_Widow | NAME_HOUSING_TYPE_House / apartment | NAME_HOUSING_TYPE_Municipal apartment | NAME_HOUSING_TYPE_Office apartment | NAME_HOUSING_TYPE_Rented apartment | NAME_HOUSING_TYPE_With parents | WEEKDAY_APPR_PROCESS_START_x_MONDAY | WEEKDAY_APPR_PROCESS_START_x_SATURDAY | WEEKDAY_APPR_PROCESS_START_x_SUNDAY | WEEKDAY_APPR_PROCESS_START_x_THURSDAY | WEEKDAY_APPR_PROCESS_START_x_TUESDAY | WEEKDAY_APPR_PROCESS_START_x_WEDNESDAY | ORGANIZATION_TYPE_Agriculture | ORGANIZATION_TYPE_Bank | ORGANIZATION_TYPE_Business Entity Type 1 | ORGANIZATION_TYPE_Business Entity Type 2 | ORGANIZATION_TYPE_Business Entity Type 3 | ORGANIZATION_TYPE_Cleaning | ORGANIZATION_TYPE_Construction | ORGANIZATION_TYPE_Culture | ORGANIZATION_TYPE_Electricity | ORGANIZATION_TYPE_Emergency | ORGANIZATION_TYPE_Government | ORGANIZATION_TYPE_Hotel | ORGANIZATION_TYPE_Housing | ORGANIZATION_TYPE_Industry: type 1 | ORGANIZATION_TYPE_Industry: type 10 | ORGANIZATION_TYPE_Industry: type 11 | ORGANIZATION_TYPE_Industry: type 12 | ORGANIZATION_TYPE_Industry: type 13 | ORGANIZATION_TYPE_Industry: type 2 | ORGANIZATION_TYPE_Industry: type 3 | ORGANIZATION_TYPE_Industry: type 4 | ORGANIZATION_TYPE_Industry: type 5 | ORGANIZATION_TYPE_Industry: type 6 | ORGANIZATION_TYPE_Industry: type 7 | ORGANIZATION_TYPE_Industry: type 8 | ORGANIZATION_TYPE_Industry: type 9 | ORGANIZATION_TYPE_Insurance | ORGANIZATION_TYPE_Kindergarten | ORGANIZATION_TYPE_Legal Services | ORGANIZATION_TYPE_Medicine | ORGANIZATION_TYPE_Military | ORGANIZATION_TYPE_Mobile | ORGANIZATION_TYPE_Other | ORGANIZATION_TYPE_Police | ORGANIZATION_TYPE_Postal | ORGANIZATION_TYPE_Realtor | ORGANIZATION_TYPE_Religion | ORGANIZATION_TYPE_Restaurant | ORGANIZATION_TYPE_School | ORGANIZATION_TYPE_Security | ORGANIZATION_TYPE_Security Ministries | ORGANIZATION_TYPE_Self-employed | ORGANIZATION_TYPE_Services | ORGANIZATION_TYPE_Telecom | ORGANIZATION_TYPE_Trade: type 1 | ORGANIZATION_TYPE_Trade: type 2 | ORGANIZATION_TYPE_Trade: type 3 | ORGANIZATION_TYPE_Trade: type 4 | ORGANIZATION_TYPE_Trade: type 5 | ORGANIZATION_TYPE_Trade: type 6 | ORGANIZATION_TYPE_Trade: type 7 | ORGANIZATION_TYPE_Transport: type 1 | ORGANIZATION_TYPE_Transport: type 2 | ORGANIZATION_TYPE_Transport: type 3 | ORGANIZATION_TYPE_Transport: type 4 | ORGANIZATION_TYPE_University | ORGANIZATION_TYPE_XNA | NAME_CONTRACT_TYPE_y_Consumer loans | NAME_CONTRACT_TYPE_y_Revolving loans | WEEKDAY_APPR_PROCESS_START_y_MONDAY | WEEKDAY_APPR_PROCESS_START_y_SATURDAY | WEEKDAY_APPR_PROCESS_START_y_SUNDAY | WEEKDAY_APPR_PROCESS_START_y_THURSDAY | WEEKDAY_APPR_PROCESS_START_y_TUESDAY | WEEKDAY_APPR_PROCESS_START_y_WEDNESDAY | NAME_CASH_LOAN_PURPOSE_Business development | NAME_CASH_LOAN_PURPOSE_Buying a garage | NAME_CASH_LOAN_PURPOSE_Buying a holiday home / land | NAME_CASH_LOAN_PURPOSE_Buying a home | NAME_CASH_LOAN_PURPOSE_Buying a new car | NAME_CASH_LOAN_PURPOSE_Buying a used car | NAME_CASH_LOAN_PURPOSE_Car repairs | NAME_CASH_LOAN_PURPOSE_Education | NAME_CASH_LOAN_PURPOSE_Everyday expenses | NAME_CASH_LOAN_PURPOSE_Furniture | NAME_CASH_LOAN_PURPOSE_Gasification / water supply | NAME_CASH_LOAN_PURPOSE_Hobby | NAME_CASH_LOAN_PURPOSE_Journey | NAME_CASH_LOAN_PURPOSE_Medicine | NAME_CASH_LOAN_PURPOSE_Money for a third person | NAME_CASH_LOAN_PURPOSE_Other | NAME_CASH_LOAN_PURPOSE_Payments on other loans | NAME_CASH_LOAN_PURPOSE_Purchase of electronic equipment | NAME_CASH_LOAN_PURPOSE_Refusal to name the goal | NAME_CASH_LOAN_PURPOSE_Repairs | NAME_CASH_LOAN_PURPOSE_Urgent needs | NAME_CASH_LOAN_PURPOSE_Wedding / gift / holiday | NAME_CASH_LOAN_PURPOSE_XAP | NAME_CASH_LOAN_PURPOSE_XNA | NAME_CONTRACT_STATUS_Canceled | NAME_CONTRACT_STATUS_Refused | NAME_CONTRACT_STATUS_Unused offer | NAME_PAYMENT_TYPE_Cashless from the account of the employer | NAME_PAYMENT_TYPE_Non-cash from your account | NAME_PAYMENT_TYPE_XNA | CODE_REJECT_REASON_HC | CODE_REJECT_REASON_LIMIT | CODE_REJECT_REASON_SCO | CODE_REJECT_REASON_SCOFR | CODE_REJECT_REASON_SYSTEM | CODE_REJECT_REASON_VERIF | CODE_REJECT_REASON_XAP | CODE_REJECT_REASON_XNA | NAME_TYPE_SUITE_y_Family | NAME_TYPE_SUITE_y_Group of people | NAME_TYPE_SUITE_y_Other_A | NAME_TYPE_SUITE_y_Other_B | NAME_TYPE_SUITE_y_Spouse, partner | NAME_TYPE_SUITE_y_Unaccompanied | NAME_CLIENT_TYPE_Refreshed | NAME_CLIENT_TYPE_Repeater | NAME_CLIENT_TYPE_XNA | NAME_GOODS_CATEGORY_Animals | NAME_GOODS_CATEGORY_Audio/Video | NAME_GOODS_CATEGORY_Auto Accessories | NAME_GOODS_CATEGORY_Clothing and Accessories | NAME_GOODS_CATEGORY_Computers | NAME_GOODS_CATEGORY_Construction Materials | NAME_GOODS_CATEGORY_Consumer Electronics | NAME_GOODS_CATEGORY_Direct Sales | NAME_GOODS_CATEGORY_Education | NAME_GOODS_CATEGORY_Fitness | NAME_GOODS_CATEGORY_Furniture | NAME_GOODS_CATEGORY_Gardening | NAME_GOODS_CATEGORY_Homewares | NAME_GOODS_CATEGORY_Insurance | NAME_GOODS_CATEGORY_Jewelry | NAME_GOODS_CATEGORY_Medical Supplies | NAME_GOODS_CATEGORY_Medicine | NAME_GOODS_CATEGORY_Mobile | NAME_GOODS_CATEGORY_Office Appliances | NAME_GOODS_CATEGORY_Other | NAME_GOODS_CATEGORY_Photo / Cinema Equipment | NAME_GOODS_CATEGORY_Sport and Leisure | NAME_GOODS_CATEGORY_Tourism | NAME_GOODS_CATEGORY_Vehicles | NAME_GOODS_CATEGORY_Weapon | NAME_GOODS_CATEGORY_XNA | NAME_PORTFOLIO_Cars | NAME_PORTFOLIO_Cash | NAME_PORTFOLIO_POS | NAME_PORTFOLIO_XNA | NAME_PRODUCT_TYPE_walk-in | NAME_PRODUCT_TYPE_x-sell | CHANNEL_TYPE_Car dealer | CHANNEL_TYPE_Channel of corporate sales | CHANNEL_TYPE_Contact center | CHANNEL_TYPE_Country-wide | CHANNEL_TYPE_Credit and cash offices | CHANNEL_TYPE_Regional / Local | CHANNEL_TYPE_Stone | NAME_SELLER_INDUSTRY_Clothing | NAME_SELLER_INDUSTRY_Connectivity | NAME_SELLER_INDUSTRY_Construction | NAME_SELLER_INDUSTRY_Consumer electronics | NAME_SELLER_INDUSTRY_Furniture | NAME_SELLER_INDUSTRY_Industry | NAME_SELLER_INDUSTRY_Jewelry | NAME_SELLER_INDUSTRY_MLM partners | NAME_SELLER_INDUSTRY_Tourism | NAME_SELLER_INDUSTRY_XNA | NAME_YIELD_GROUP_high | NAME_YIELD_GROUP_low_action | NAME_YIELD_GROUP_low_normal | NAME_YIELD_GROUP_middle | PRODUCT_COMBINATION_Card X-Sell | PRODUCT_COMBINATION_Cash | PRODUCT_COMBINATION_Cash Street: high | PRODUCT_COMBINATION_Cash Street: low | PRODUCT_COMBINATION_Cash Street: middle | PRODUCT_COMBINATION_Cash X-Sell: high | PRODUCT_COMBINATION_Cash X-Sell: low | PRODUCT_COMBINATION_Cash X-Sell: middle | PRODUCT_COMBINATION_POS household with interest | PRODUCT_COMBINATION_POS household without interest | PRODUCT_COMBINATION_POS industry with interest | PRODUCT_COMBINATION_POS industry without interest | PRODUCT_COMBINATION_POS mobile with interest | PRODUCT_COMBINATION_POS mobile without interest | PRODUCT_COMBINATION_POS other with interest | PRODUCT_COMBINATION_POS others without interest | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Cash loans | M | 0 | 1 | 0 | 202500.0 | 406597.5 | 24700.5 | 351000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.018801 | 9461 | -637 | -3648.0 | 2120 | 1 | 1 | 0 | 1 | 1 | 0 | 1.0 | 2 | 2 | WEDNESDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 2.0 | 2.0 | 2.0 | 2.0 | 1134.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | Consumer loans | 9251.775 | 179055.0 | 179055.0 | 179055.0 | SATURDAY | 9 | 1 | 1 | XAP | Approved | -606 | XNA | XAP | Unaccompanied | New | Vehicles | POS | XNA | Stone | 500 | Auto technology | 24.0 | low_normal | POS other with interest | 365243.0 | -565.0 | 125.0 | -25.0 | -17.0 | 0.0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 1 | 0 | Cash loans | F | 0 | 0 | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | Cash loans | 98356.995 | 900000.0 | 1035882.0 | 900000.0 | FRIDAY | 12 | 1 | 1 | XNA | Approved | -746 | XNA | XAP | Unaccompanied | Repeater | XNA | Cash | x-sell | Credit and cash offices | -1 | XNA | 12.0 | low_normal | Cash X-Sell: low | 365243.0 | -716.0 | -386.0 | -536.0 | -527.0 | 1.0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | Cash loans | F | 0 | 0 | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | Consumer loans | 64567.665 | 337500.0 | 348637.5 | 337500.0 | SUNDAY | 17 | 1 | 1 | XAP | Approved | -828 | Cash through the bank | XAP | Family | Refreshed | Furniture | POS | XNA | Stone | 1400 | Furniture | 6.0 | middle | POS industry with interest | 365243.0 | -797.0 | -647.0 | -647.0 | -639.0 | 0.0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 3 | 0 | Cash loans | F | 0 | 0 | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 1 | 1 | 0 | 1 | 1 | 0 | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | Consumer loans | 6737.310 | 68809.5 | 68053.5 | 68809.5 | SATURDAY | 15 | 1 | 1 | XAP | Approved | -2341 | Cash through the bank | XAP | Family | Refreshed | Consumer Electronics | POS | XNA | Country-wide | 200 | Consumer electronics | 12.0 | middle | POS household with interest | 365243.0 | -2310.0 | -1980.0 | -1980.0 | -1976.0 | 1.0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | Revolving loans | M | 1 | 1 | 0 | 67500.0 | 135000.0 | 6750.0 | 135000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.010032 | 19046 | -225 | -4260.0 | 2531 | 1 | 1 | 1 | 1 | 1 | 0 | 1.0 | 2 | 2 | MONDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Government | 0.0 | 0.0 | 0.0 | 0.0 | 815.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | Consumer loans | 5357.250 | 24282.0 | 20106.0 | 24282.0 | FRIDAY | 5 | 1 | 1 | XAP | Approved | -815 | Cash through the bank | XAP | Unaccompanied | New | Mobile | POS | XNA | Regional / Local | 30 | Connectivity | 4.0 | middle | POS mobile without interest | 365243.0 | -784.0 | -694.0 | -724.0 | -714.0 | 0.0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
mergeddf = mergeddf.drop(['FLAG_OWN_CAR','FLAG_OWN_REALTY','FLAG_MOBIL',
'FLAG_EMP_PHONE','FLAG_WORK_PHONE','FLAG_CONT_MOBILE',
'FLAG_PHONE','FLAG_EMAIL','REGION_RATING_CLIENT',
'REGION_RATING_CLIENT_W_CITY','REG_REGION_NOT_LIVE_REGION',
'REG_REGION_NOT_WORK_REGION','LIVE_REGION_NOT_WORK_REGION',
'REG_CITY_NOT_LIVE_CITY','REG_CITY_NOT_WORK_CITY','FLAG_DOCUMENT_2',
'FLAG_DOCUMENT_3','FLAG_DOCUMENT_4','FLAG_DOCUMENT_5','FLAG_DOCUMENT_6',
'FLAG_DOCUMENT_7','FLAG_DOCUMENT_8','FLAG_DOCUMENT_9',
'FLAG_DOCUMENT_10','FLAG_DOCUMENT_11','FLAG_DOCUMENT_12','FLAG_DOCUMENT_13',
'FLAG_DOCUMENT_14','FLAG_DOCUMENT_15','FLAG_DOCUMENT_16','FLAG_DOCUMENT_17',
'FLAG_DOCUMENT_18','FLAG_DOCUMENT_19','FLAG_DOCUMENT_20','FLAG_DOCUMENT_21',
'NFLAG_INSURED_ON_APPROVAL','NAME_CONTRACT_TYPE_x','CODE_GENDER',
'NAME_TYPE_SUITE_x','NAME_INCOME_TYPE','NAME_EDUCATION_TYPE','NAME_FAMILY_STATUS',
'NAME_HOUSING_TYPE','WEEKDAY_APPR_PROCESS_START_x','ORGANIZATION_TYPE','NAME_CONTRACT_TYPE_y',
'WEEKDAY_APPR_PROCESS_START_y','NAME_CASH_LOAN_PURPOSE','NAME_CONTRACT_STATUS','NAME_PAYMENT_TYPE',
'CODE_REJECT_REASON','NAME_TYPE_SUITE_y','NAME_CLIENT_TYPE','NAME_GOODS_CATEGORY',
'NAME_PORTFOLIO','NAME_PRODUCT_TYPE','CHANNEL_TYPE','NAME_SELLER_INDUSTRY',
'NAME_YIELD_GROUP','PRODUCT_COMBINATION'], axis = 1)
mergeddf.head()
| TARGET | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT_x | AMT_ANNUITY_x | AMT_GOODS_PRICE_x | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | CNT_FAM_MEMBERS | HOUR_APPR_PROCESS_START_x | LIVE_CITY_NOT_WORK_CITY | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | AMT_ANNUITY_y | AMT_APPLICATION | AMT_CREDIT_y | AMT_GOODS_PRICE_y | HOUR_APPR_PROCESS_START_y | FLAG_LAST_APPL_PER_CONTRACT | NFLAG_LAST_APPL_IN_DAY | DAYS_DECISION | SELLERPLACE_AREA | CNT_PAYMENT | DAYS_FIRST_DRAWING | DAYS_FIRST_DUE | DAYS_LAST_DUE_1ST_VERSION | DAYS_LAST_DUE | DAYS_TERMINATION | FLAG_OWN_CAR_1 | FLAG_OWN_REALTY_1 | FLAG_EMP_PHONE_1 | FLAG_WORK_PHONE_1 | FLAG_CONT_MOBILE_1 | FLAG_PHONE_1 | FLAG_EMAIL_1 | REGION_RATING_CLIENT_2 | REGION_RATING_CLIENT_3 | REGION_RATING_CLIENT_W_CITY_2 | REGION_RATING_CLIENT_W_CITY_3 | REG_REGION_NOT_LIVE_REGION_1 | REG_REGION_NOT_WORK_REGION_1 | LIVE_REGION_NOT_WORK_REGION_1 | REG_CITY_NOT_LIVE_CITY_1 | REG_CITY_NOT_WORK_CITY_1 | FLAG_DOCUMENT_2_1 | FLAG_DOCUMENT_3_1 | FLAG_DOCUMENT_4_1 | FLAG_DOCUMENT_5_1 | FLAG_DOCUMENT_6_1 | FLAG_DOCUMENT_7_1 | FLAG_DOCUMENT_8_1 | FLAG_DOCUMENT_9_1 | FLAG_DOCUMENT_10_1 | FLAG_DOCUMENT_11_1 | FLAG_DOCUMENT_12_1 | FLAG_DOCUMENT_13_1 | FLAG_DOCUMENT_14_1 | FLAG_DOCUMENT_15_1 | FLAG_DOCUMENT_16_1 | FLAG_DOCUMENT_17_1 | FLAG_DOCUMENT_18_1 | FLAG_DOCUMENT_19_1 | FLAG_DOCUMENT_20_1 | FLAG_DOCUMENT_21_1 | NFLAG_INSURED_ON_APPROVAL_1.0 | NAME_CONTRACT_TYPE_x_Revolving loans | CODE_GENDER_M | CODE_GENDER_XNA | NAME_TYPE_SUITE_x_Family | NAME_TYPE_SUITE_x_Group of people | NAME_TYPE_SUITE_x_Other_A | NAME_TYPE_SUITE_x_Other_B | NAME_TYPE_SUITE_x_Spouse, partner | NAME_TYPE_SUITE_x_Unaccompanied | NAME_INCOME_TYPE_Maternity leave | NAME_INCOME_TYPE_Pensioner | NAME_INCOME_TYPE_State servant | NAME_INCOME_TYPE_Student | NAME_INCOME_TYPE_Unemployed | NAME_INCOME_TYPE_Working | NAME_EDUCATION_TYPE_Higher education | NAME_EDUCATION_TYPE_Incomplete higher | NAME_EDUCATION_TYPE_Lower secondary | NAME_EDUCATION_TYPE_Secondary / secondary special | NAME_FAMILY_STATUS_Married | NAME_FAMILY_STATUS_Separated | NAME_FAMILY_STATUS_Single / not married | NAME_FAMILY_STATUS_Widow | NAME_HOUSING_TYPE_House / apartment | NAME_HOUSING_TYPE_Municipal apartment | NAME_HOUSING_TYPE_Office apartment | NAME_HOUSING_TYPE_Rented apartment | NAME_HOUSING_TYPE_With parents | WEEKDAY_APPR_PROCESS_START_x_MONDAY | WEEKDAY_APPR_PROCESS_START_x_SATURDAY | WEEKDAY_APPR_PROCESS_START_x_SUNDAY | WEEKDAY_APPR_PROCESS_START_x_THURSDAY | WEEKDAY_APPR_PROCESS_START_x_TUESDAY | WEEKDAY_APPR_PROCESS_START_x_WEDNESDAY | ORGANIZATION_TYPE_Agriculture | ORGANIZATION_TYPE_Bank | ORGANIZATION_TYPE_Business Entity Type 1 | ORGANIZATION_TYPE_Business Entity Type 2 | ORGANIZATION_TYPE_Business Entity Type 3 | ORGANIZATION_TYPE_Cleaning | ORGANIZATION_TYPE_Construction | ORGANIZATION_TYPE_Culture | ORGANIZATION_TYPE_Electricity | ORGANIZATION_TYPE_Emergency | ORGANIZATION_TYPE_Government | ORGANIZATION_TYPE_Hotel | ORGANIZATION_TYPE_Housing | ORGANIZATION_TYPE_Industry: type 1 | ORGANIZATION_TYPE_Industry: type 10 | ORGANIZATION_TYPE_Industry: type 11 | ORGANIZATION_TYPE_Industry: type 12 | ORGANIZATION_TYPE_Industry: type 13 | ORGANIZATION_TYPE_Industry: type 2 | ORGANIZATION_TYPE_Industry: type 3 | ORGANIZATION_TYPE_Industry: type 4 | ORGANIZATION_TYPE_Industry: type 5 | ORGANIZATION_TYPE_Industry: type 6 | ORGANIZATION_TYPE_Industry: type 7 | ORGANIZATION_TYPE_Industry: type 8 | ORGANIZATION_TYPE_Industry: type 9 | ORGANIZATION_TYPE_Insurance | ORGANIZATION_TYPE_Kindergarten | ORGANIZATION_TYPE_Legal Services | ORGANIZATION_TYPE_Medicine | ORGANIZATION_TYPE_Military | ORGANIZATION_TYPE_Mobile | ORGANIZATION_TYPE_Other | ORGANIZATION_TYPE_Police | ORGANIZATION_TYPE_Postal | ORGANIZATION_TYPE_Realtor | ORGANIZATION_TYPE_Religion | ORGANIZATION_TYPE_Restaurant | ORGANIZATION_TYPE_School | ORGANIZATION_TYPE_Security | ORGANIZATION_TYPE_Security Ministries | ORGANIZATION_TYPE_Self-employed | ORGANIZATION_TYPE_Services | ORGANIZATION_TYPE_Telecom | ORGANIZATION_TYPE_Trade: type 1 | ORGANIZATION_TYPE_Trade: type 2 | ORGANIZATION_TYPE_Trade: type 3 | ORGANIZATION_TYPE_Trade: type 4 | ORGANIZATION_TYPE_Trade: type 5 | ORGANIZATION_TYPE_Trade: type 6 | ORGANIZATION_TYPE_Trade: type 7 | ORGANIZATION_TYPE_Transport: type 1 | ORGANIZATION_TYPE_Transport: type 2 | ORGANIZATION_TYPE_Transport: type 3 | ORGANIZATION_TYPE_Transport: type 4 | ORGANIZATION_TYPE_University | ORGANIZATION_TYPE_XNA | NAME_CONTRACT_TYPE_y_Consumer loans | NAME_CONTRACT_TYPE_y_Revolving loans | WEEKDAY_APPR_PROCESS_START_y_MONDAY | WEEKDAY_APPR_PROCESS_START_y_SATURDAY | WEEKDAY_APPR_PROCESS_START_y_SUNDAY | WEEKDAY_APPR_PROCESS_START_y_THURSDAY | WEEKDAY_APPR_PROCESS_START_y_TUESDAY | WEEKDAY_APPR_PROCESS_START_y_WEDNESDAY | NAME_CASH_LOAN_PURPOSE_Business development | NAME_CASH_LOAN_PURPOSE_Buying a garage | NAME_CASH_LOAN_PURPOSE_Buying a holiday home / land | NAME_CASH_LOAN_PURPOSE_Buying a home | NAME_CASH_LOAN_PURPOSE_Buying a new car | NAME_CASH_LOAN_PURPOSE_Buying a used car | NAME_CASH_LOAN_PURPOSE_Car repairs | NAME_CASH_LOAN_PURPOSE_Education | NAME_CASH_LOAN_PURPOSE_Everyday expenses | NAME_CASH_LOAN_PURPOSE_Furniture | NAME_CASH_LOAN_PURPOSE_Gasification / water supply | NAME_CASH_LOAN_PURPOSE_Hobby | NAME_CASH_LOAN_PURPOSE_Journey | NAME_CASH_LOAN_PURPOSE_Medicine | NAME_CASH_LOAN_PURPOSE_Money for a third person | NAME_CASH_LOAN_PURPOSE_Other | NAME_CASH_LOAN_PURPOSE_Payments on other loans | NAME_CASH_LOAN_PURPOSE_Purchase of electronic equipment | NAME_CASH_LOAN_PURPOSE_Refusal to name the goal | NAME_CASH_LOAN_PURPOSE_Repairs | NAME_CASH_LOAN_PURPOSE_Urgent needs | NAME_CASH_LOAN_PURPOSE_Wedding / gift / holiday | NAME_CASH_LOAN_PURPOSE_XAP | NAME_CASH_LOAN_PURPOSE_XNA | NAME_CONTRACT_STATUS_Canceled | NAME_CONTRACT_STATUS_Refused | NAME_CONTRACT_STATUS_Unused offer | NAME_PAYMENT_TYPE_Cashless from the account of the employer | NAME_PAYMENT_TYPE_Non-cash from your account | NAME_PAYMENT_TYPE_XNA | CODE_REJECT_REASON_HC | CODE_REJECT_REASON_LIMIT | CODE_REJECT_REASON_SCO | CODE_REJECT_REASON_SCOFR | CODE_REJECT_REASON_SYSTEM | CODE_REJECT_REASON_VERIF | CODE_REJECT_REASON_XAP | CODE_REJECT_REASON_XNA | NAME_TYPE_SUITE_y_Family | NAME_TYPE_SUITE_y_Group of people | NAME_TYPE_SUITE_y_Other_A | NAME_TYPE_SUITE_y_Other_B | NAME_TYPE_SUITE_y_Spouse, partner | NAME_TYPE_SUITE_y_Unaccompanied | NAME_CLIENT_TYPE_Refreshed | NAME_CLIENT_TYPE_Repeater | NAME_CLIENT_TYPE_XNA | NAME_GOODS_CATEGORY_Animals | NAME_GOODS_CATEGORY_Audio/Video | NAME_GOODS_CATEGORY_Auto Accessories | NAME_GOODS_CATEGORY_Clothing and Accessories | NAME_GOODS_CATEGORY_Computers | NAME_GOODS_CATEGORY_Construction Materials | NAME_GOODS_CATEGORY_Consumer Electronics | NAME_GOODS_CATEGORY_Direct Sales | NAME_GOODS_CATEGORY_Education | NAME_GOODS_CATEGORY_Fitness | NAME_GOODS_CATEGORY_Furniture | NAME_GOODS_CATEGORY_Gardening | NAME_GOODS_CATEGORY_Homewares | NAME_GOODS_CATEGORY_Insurance | NAME_GOODS_CATEGORY_Jewelry | NAME_GOODS_CATEGORY_Medical Supplies | NAME_GOODS_CATEGORY_Medicine | NAME_GOODS_CATEGORY_Mobile | NAME_GOODS_CATEGORY_Office Appliances | NAME_GOODS_CATEGORY_Other | NAME_GOODS_CATEGORY_Photo / Cinema Equipment | NAME_GOODS_CATEGORY_Sport and Leisure | NAME_GOODS_CATEGORY_Tourism | NAME_GOODS_CATEGORY_Vehicles | NAME_GOODS_CATEGORY_Weapon | NAME_GOODS_CATEGORY_XNA | NAME_PORTFOLIO_Cars | NAME_PORTFOLIO_Cash | NAME_PORTFOLIO_POS | NAME_PORTFOLIO_XNA | NAME_PRODUCT_TYPE_walk-in | NAME_PRODUCT_TYPE_x-sell | CHANNEL_TYPE_Car dealer | CHANNEL_TYPE_Channel of corporate sales | CHANNEL_TYPE_Contact center | CHANNEL_TYPE_Country-wide | CHANNEL_TYPE_Credit and cash offices | CHANNEL_TYPE_Regional / Local | CHANNEL_TYPE_Stone | NAME_SELLER_INDUSTRY_Clothing | NAME_SELLER_INDUSTRY_Connectivity | NAME_SELLER_INDUSTRY_Construction | NAME_SELLER_INDUSTRY_Consumer electronics | NAME_SELLER_INDUSTRY_Furniture | NAME_SELLER_INDUSTRY_Industry | NAME_SELLER_INDUSTRY_Jewelry | NAME_SELLER_INDUSTRY_MLM partners | NAME_SELLER_INDUSTRY_Tourism | NAME_SELLER_INDUSTRY_XNA | NAME_YIELD_GROUP_high | NAME_YIELD_GROUP_low_action | NAME_YIELD_GROUP_low_normal | NAME_YIELD_GROUP_middle | PRODUCT_COMBINATION_Card X-Sell | PRODUCT_COMBINATION_Cash | PRODUCT_COMBINATION_Cash Street: high | PRODUCT_COMBINATION_Cash Street: low | PRODUCT_COMBINATION_Cash Street: middle | PRODUCT_COMBINATION_Cash X-Sell: high | PRODUCT_COMBINATION_Cash X-Sell: low | PRODUCT_COMBINATION_Cash X-Sell: middle | PRODUCT_COMBINATION_POS household with interest | PRODUCT_COMBINATION_POS household without interest | PRODUCT_COMBINATION_POS industry with interest | PRODUCT_COMBINATION_POS industry without interest | PRODUCT_COMBINATION_POS mobile with interest | PRODUCT_COMBINATION_POS mobile without interest | PRODUCT_COMBINATION_POS other with interest | PRODUCT_COMBINATION_POS others without interest | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 202500.0 | 406597.5 | 24700.5 | 351000.0 | 0.018801 | 9461 | -637 | -3648.0 | 2120 | 1.0 | 10 | 0 | 2.0 | 2.0 | 2.0 | 2.0 | 1134.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 9251.775 | 179055.0 | 179055.0 | 179055.0 | 9 | 1 | 1 | -606 | 500 | 24.0 | 365243.0 | -565.0 | 125.0 | -25.0 | -17.0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 1 | 0 | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 2.0 | 11 | 0 | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 98356.995 | 900000.0 | 1035882.0 | 900000.0 | 12 | 1 | 1 | -746 | -1 | 12.0 | 365243.0 | -716.0 | -386.0 | -536.0 | -527.0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 2.0 | 11 | 0 | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 64567.665 | 337500.0 | 348637.5 | 337500.0 | 17 | 1 | 1 | -828 | 1400 | 6.0 | 365243.0 | -797.0 | -647.0 | -647.0 | -639.0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 3 | 0 | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | 0.003541 | 16765 | -1188 | -1186.0 | 291 | 2.0 | 11 | 0 | 1.0 | 0.0 | 1.0 | 0.0 | 828.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6737.310 | 68809.5 | 68053.5 | 68809.5 | 15 | 1 | 1 | -2341 | 200 | 12.0 | 365243.0 | -2310.0 | -1980.0 | -1980.0 | -1976.0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 67500.0 | 135000.0 | 6750.0 | 135000.0 | 0.010032 | 19046 | -225 | -4260.0 | 2531 | 1.0 | 9 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 815.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 5357.250 | 24282.0 | 20106.0 | 24282.0 | 5 | 1 | 1 | -815 | 30 | 4.0 | 365243.0 | -784.0 | -694.0 | -724.0 | -714.0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
mergeddf.shape
(1406625, 292)
mergeddfs=mergeddf.sample(n = 7000)
from sklearn.model_selection import train_test_split
# Putting feature variable to X
X = mergeddfs.drop(['TARGET'], axis=1)
X.head()
| CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT_x | AMT_ANNUITY_x | AMT_GOODS_PRICE_x | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | CNT_FAM_MEMBERS | HOUR_APPR_PROCESS_START_x | LIVE_CITY_NOT_WORK_CITY | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | AMT_ANNUITY_y | AMT_APPLICATION | AMT_CREDIT_y | AMT_GOODS_PRICE_y | HOUR_APPR_PROCESS_START_y | FLAG_LAST_APPL_PER_CONTRACT | NFLAG_LAST_APPL_IN_DAY | DAYS_DECISION | SELLERPLACE_AREA | CNT_PAYMENT | DAYS_FIRST_DRAWING | DAYS_FIRST_DUE | DAYS_LAST_DUE_1ST_VERSION | DAYS_LAST_DUE | DAYS_TERMINATION | FLAG_OWN_CAR_1 | FLAG_OWN_REALTY_1 | FLAG_EMP_PHONE_1 | FLAG_WORK_PHONE_1 | FLAG_CONT_MOBILE_1 | FLAG_PHONE_1 | FLAG_EMAIL_1 | REGION_RATING_CLIENT_2 | REGION_RATING_CLIENT_3 | REGION_RATING_CLIENT_W_CITY_2 | REGION_RATING_CLIENT_W_CITY_3 | REG_REGION_NOT_LIVE_REGION_1 | REG_REGION_NOT_WORK_REGION_1 | LIVE_REGION_NOT_WORK_REGION_1 | REG_CITY_NOT_LIVE_CITY_1 | REG_CITY_NOT_WORK_CITY_1 | FLAG_DOCUMENT_2_1 | FLAG_DOCUMENT_3_1 | FLAG_DOCUMENT_4_1 | FLAG_DOCUMENT_5_1 | FLAG_DOCUMENT_6_1 | FLAG_DOCUMENT_7_1 | FLAG_DOCUMENT_8_1 | FLAG_DOCUMENT_9_1 | FLAG_DOCUMENT_10_1 | FLAG_DOCUMENT_11_1 | FLAG_DOCUMENT_12_1 | FLAG_DOCUMENT_13_1 | FLAG_DOCUMENT_14_1 | FLAG_DOCUMENT_15_1 | FLAG_DOCUMENT_16_1 | FLAG_DOCUMENT_17_1 | FLAG_DOCUMENT_18_1 | FLAG_DOCUMENT_19_1 | FLAG_DOCUMENT_20_1 | FLAG_DOCUMENT_21_1 | NFLAG_INSURED_ON_APPROVAL_1.0 | NAME_CONTRACT_TYPE_x_Revolving loans | CODE_GENDER_M | CODE_GENDER_XNA | NAME_TYPE_SUITE_x_Family | NAME_TYPE_SUITE_x_Group of people | NAME_TYPE_SUITE_x_Other_A | NAME_TYPE_SUITE_x_Other_B | NAME_TYPE_SUITE_x_Spouse, partner | NAME_TYPE_SUITE_x_Unaccompanied | NAME_INCOME_TYPE_Maternity leave | NAME_INCOME_TYPE_Pensioner | NAME_INCOME_TYPE_State servant | NAME_INCOME_TYPE_Student | NAME_INCOME_TYPE_Unemployed | NAME_INCOME_TYPE_Working | NAME_EDUCATION_TYPE_Higher education | NAME_EDUCATION_TYPE_Incomplete higher | NAME_EDUCATION_TYPE_Lower secondary | NAME_EDUCATION_TYPE_Secondary / secondary special | NAME_FAMILY_STATUS_Married | NAME_FAMILY_STATUS_Separated | NAME_FAMILY_STATUS_Single / not married | NAME_FAMILY_STATUS_Widow | NAME_HOUSING_TYPE_House / apartment | NAME_HOUSING_TYPE_Municipal apartment | NAME_HOUSING_TYPE_Office apartment | NAME_HOUSING_TYPE_Rented apartment | NAME_HOUSING_TYPE_With parents | WEEKDAY_APPR_PROCESS_START_x_MONDAY | WEEKDAY_APPR_PROCESS_START_x_SATURDAY | WEEKDAY_APPR_PROCESS_START_x_SUNDAY | WEEKDAY_APPR_PROCESS_START_x_THURSDAY | WEEKDAY_APPR_PROCESS_START_x_TUESDAY | WEEKDAY_APPR_PROCESS_START_x_WEDNESDAY | ORGANIZATION_TYPE_Agriculture | ORGANIZATION_TYPE_Bank | ORGANIZATION_TYPE_Business Entity Type 1 | ORGANIZATION_TYPE_Business Entity Type 2 | ORGANIZATION_TYPE_Business Entity Type 3 | ORGANIZATION_TYPE_Cleaning | ORGANIZATION_TYPE_Construction | ORGANIZATION_TYPE_Culture | ORGANIZATION_TYPE_Electricity | ORGANIZATION_TYPE_Emergency | ORGANIZATION_TYPE_Government | ORGANIZATION_TYPE_Hotel | ORGANIZATION_TYPE_Housing | ORGANIZATION_TYPE_Industry: type 1 | ORGANIZATION_TYPE_Industry: type 10 | ORGANIZATION_TYPE_Industry: type 11 | ORGANIZATION_TYPE_Industry: type 12 | ORGANIZATION_TYPE_Industry: type 13 | ORGANIZATION_TYPE_Industry: type 2 | ORGANIZATION_TYPE_Industry: type 3 | ORGANIZATION_TYPE_Industry: type 4 | ORGANIZATION_TYPE_Industry: type 5 | ORGANIZATION_TYPE_Industry: type 6 | ORGANIZATION_TYPE_Industry: type 7 | ORGANIZATION_TYPE_Industry: type 8 | ORGANIZATION_TYPE_Industry: type 9 | ORGANIZATION_TYPE_Insurance | ORGANIZATION_TYPE_Kindergarten | ORGANIZATION_TYPE_Legal Services | ORGANIZATION_TYPE_Medicine | ORGANIZATION_TYPE_Military | ORGANIZATION_TYPE_Mobile | ORGANIZATION_TYPE_Other | ORGANIZATION_TYPE_Police | ORGANIZATION_TYPE_Postal | ORGANIZATION_TYPE_Realtor | ORGANIZATION_TYPE_Religion | ORGANIZATION_TYPE_Restaurant | ORGANIZATION_TYPE_School | ORGANIZATION_TYPE_Security | ORGANIZATION_TYPE_Security Ministries | ORGANIZATION_TYPE_Self-employed | ORGANIZATION_TYPE_Services | ORGANIZATION_TYPE_Telecom | ORGANIZATION_TYPE_Trade: type 1 | ORGANIZATION_TYPE_Trade: type 2 | ORGANIZATION_TYPE_Trade: type 3 | ORGANIZATION_TYPE_Trade: type 4 | ORGANIZATION_TYPE_Trade: type 5 | ORGANIZATION_TYPE_Trade: type 6 | ORGANIZATION_TYPE_Trade: type 7 | ORGANIZATION_TYPE_Transport: type 1 | ORGANIZATION_TYPE_Transport: type 2 | ORGANIZATION_TYPE_Transport: type 3 | ORGANIZATION_TYPE_Transport: type 4 | ORGANIZATION_TYPE_University | ORGANIZATION_TYPE_XNA | NAME_CONTRACT_TYPE_y_Consumer loans | NAME_CONTRACT_TYPE_y_Revolving loans | WEEKDAY_APPR_PROCESS_START_y_MONDAY | WEEKDAY_APPR_PROCESS_START_y_SATURDAY | WEEKDAY_APPR_PROCESS_START_y_SUNDAY | WEEKDAY_APPR_PROCESS_START_y_THURSDAY | WEEKDAY_APPR_PROCESS_START_y_TUESDAY | WEEKDAY_APPR_PROCESS_START_y_WEDNESDAY | NAME_CASH_LOAN_PURPOSE_Business development | NAME_CASH_LOAN_PURPOSE_Buying a garage | NAME_CASH_LOAN_PURPOSE_Buying a holiday home / land | NAME_CASH_LOAN_PURPOSE_Buying a home | NAME_CASH_LOAN_PURPOSE_Buying a new car | NAME_CASH_LOAN_PURPOSE_Buying a used car | NAME_CASH_LOAN_PURPOSE_Car repairs | NAME_CASH_LOAN_PURPOSE_Education | NAME_CASH_LOAN_PURPOSE_Everyday expenses | NAME_CASH_LOAN_PURPOSE_Furniture | NAME_CASH_LOAN_PURPOSE_Gasification / water supply | NAME_CASH_LOAN_PURPOSE_Hobby | NAME_CASH_LOAN_PURPOSE_Journey | NAME_CASH_LOAN_PURPOSE_Medicine | NAME_CASH_LOAN_PURPOSE_Money for a third person | NAME_CASH_LOAN_PURPOSE_Other | NAME_CASH_LOAN_PURPOSE_Payments on other loans | NAME_CASH_LOAN_PURPOSE_Purchase of electronic equipment | NAME_CASH_LOAN_PURPOSE_Refusal to name the goal | NAME_CASH_LOAN_PURPOSE_Repairs | NAME_CASH_LOAN_PURPOSE_Urgent needs | NAME_CASH_LOAN_PURPOSE_Wedding / gift / holiday | NAME_CASH_LOAN_PURPOSE_XAP | NAME_CASH_LOAN_PURPOSE_XNA | NAME_CONTRACT_STATUS_Canceled | NAME_CONTRACT_STATUS_Refused | NAME_CONTRACT_STATUS_Unused offer | NAME_PAYMENT_TYPE_Cashless from the account of the employer | NAME_PAYMENT_TYPE_Non-cash from your account | NAME_PAYMENT_TYPE_XNA | CODE_REJECT_REASON_HC | CODE_REJECT_REASON_LIMIT | CODE_REJECT_REASON_SCO | CODE_REJECT_REASON_SCOFR | CODE_REJECT_REASON_SYSTEM | CODE_REJECT_REASON_VERIF | CODE_REJECT_REASON_XAP | CODE_REJECT_REASON_XNA | NAME_TYPE_SUITE_y_Family | NAME_TYPE_SUITE_y_Group of people | NAME_TYPE_SUITE_y_Other_A | NAME_TYPE_SUITE_y_Other_B | NAME_TYPE_SUITE_y_Spouse, partner | NAME_TYPE_SUITE_y_Unaccompanied | NAME_CLIENT_TYPE_Refreshed | NAME_CLIENT_TYPE_Repeater | NAME_CLIENT_TYPE_XNA | NAME_GOODS_CATEGORY_Animals | NAME_GOODS_CATEGORY_Audio/Video | NAME_GOODS_CATEGORY_Auto Accessories | NAME_GOODS_CATEGORY_Clothing and Accessories | NAME_GOODS_CATEGORY_Computers | NAME_GOODS_CATEGORY_Construction Materials | NAME_GOODS_CATEGORY_Consumer Electronics | NAME_GOODS_CATEGORY_Direct Sales | NAME_GOODS_CATEGORY_Education | NAME_GOODS_CATEGORY_Fitness | NAME_GOODS_CATEGORY_Furniture | NAME_GOODS_CATEGORY_Gardening | NAME_GOODS_CATEGORY_Homewares | NAME_GOODS_CATEGORY_Insurance | NAME_GOODS_CATEGORY_Jewelry | NAME_GOODS_CATEGORY_Medical Supplies | NAME_GOODS_CATEGORY_Medicine | NAME_GOODS_CATEGORY_Mobile | NAME_GOODS_CATEGORY_Office Appliances | NAME_GOODS_CATEGORY_Other | NAME_GOODS_CATEGORY_Photo / Cinema Equipment | NAME_GOODS_CATEGORY_Sport and Leisure | NAME_GOODS_CATEGORY_Tourism | NAME_GOODS_CATEGORY_Vehicles | NAME_GOODS_CATEGORY_Weapon | NAME_GOODS_CATEGORY_XNA | NAME_PORTFOLIO_Cars | NAME_PORTFOLIO_Cash | NAME_PORTFOLIO_POS | NAME_PORTFOLIO_XNA | NAME_PRODUCT_TYPE_walk-in | NAME_PRODUCT_TYPE_x-sell | CHANNEL_TYPE_Car dealer | CHANNEL_TYPE_Channel of corporate sales | CHANNEL_TYPE_Contact center | CHANNEL_TYPE_Country-wide | CHANNEL_TYPE_Credit and cash offices | CHANNEL_TYPE_Regional / Local | CHANNEL_TYPE_Stone | NAME_SELLER_INDUSTRY_Clothing | NAME_SELLER_INDUSTRY_Connectivity | NAME_SELLER_INDUSTRY_Construction | NAME_SELLER_INDUSTRY_Consumer electronics | NAME_SELLER_INDUSTRY_Furniture | NAME_SELLER_INDUSTRY_Industry | NAME_SELLER_INDUSTRY_Jewelry | NAME_SELLER_INDUSTRY_MLM partners | NAME_SELLER_INDUSTRY_Tourism | NAME_SELLER_INDUSTRY_XNA | NAME_YIELD_GROUP_high | NAME_YIELD_GROUP_low_action | NAME_YIELD_GROUP_low_normal | NAME_YIELD_GROUP_middle | PRODUCT_COMBINATION_Card X-Sell | PRODUCT_COMBINATION_Cash | PRODUCT_COMBINATION_Cash Street: high | PRODUCT_COMBINATION_Cash Street: low | PRODUCT_COMBINATION_Cash Street: middle | PRODUCT_COMBINATION_Cash X-Sell: high | PRODUCT_COMBINATION_Cash X-Sell: low | PRODUCT_COMBINATION_Cash X-Sell: middle | PRODUCT_COMBINATION_POS household with interest | PRODUCT_COMBINATION_POS household without interest | PRODUCT_COMBINATION_POS industry with interest | PRODUCT_COMBINATION_POS industry without interest | PRODUCT_COMBINATION_POS mobile with interest | PRODUCT_COMBINATION_POS mobile without interest | PRODUCT_COMBINATION_POS other with interest | PRODUCT_COMBINATION_POS others without interest | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 620985 | 2 | 87750.0 | 314100.0 | 16573.5 | 225000.0 | 0.018634 | 10856 | -1455 | -2167.0 | 3394 | 4.0 | 13 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 1202.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 | 10180.215000 | 90000.0 | 95940.0 | 90000.000000 | 11 | 1 | 1 | -146 | 53 | 12.0 | 365243.0 | -116.0 | 214.0 | 365243.0 | 365243.0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1030348 | 0 | 90000.0 | 135000.0 | 6750.0 | 135000.0 | 0.010966 | 17953 | -631 | -11317.0 | 1302 | 2.0 | 11 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 178.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 15837.184952 | 0.0 | 0.0 | 226451.191283 | 12 | 1 | 1 | -142 | 30 | 12.0 | 365243.0 | -825.0 | -358.0 | -534.0 | -494.0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 206555 | 1 | 225000.0 | 906615.0 | 30091.5 | 688500.0 | 0.032561 | 15346 | -5040 | -5071.0 | 1121 | 3.0 | 14 | 0 | 1.0 | 0.0 | 1.0 | 0.0 | 120.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 | 1.0 | 15837.184952 | 0.0 | 0.0 | 226451.191283 | 13 | 1 | 1 | -120 | -1 | 12.0 | 365243.0 | -825.0 | -358.0 | -534.0 | -494.0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1257561 | 0 | 180000.0 | 495351.0 | 28566.0 | 459000.0 | 0.018634 | 19463 | -11799 | -6945.0 | 3001 | 2.0 | 10 | 0 | 2.0 | 0.0 | 2.0 | 0.0 | 3485.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 15837.184952 | 0.0 | 0.0 | 226451.191283 | 14 | 1 | 1 | -7 | -1 | 12.0 | 365243.0 | -825.0 | -358.0 | -534.0 | -494.0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1283370 | 0 | 135000.0 | 835605.0 | 24561.0 | 697500.0 | 0.019689 | 16011 | -951 | -5757.0 | 4664 | 2.0 | 11 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1932.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 5110.020000 | 38916.0 | 26928.0 | 38916.000000 | 9 | 1 | 1 | -259 | 1000 | 6.0 | 365243.0 | -228.0 | -78.0 | -78.0 | -76.0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
X.shape
(7000, 291)
y = mergeddfs['TARGET']
y.head()
620985 0 1030348 1 206555 0 1257561 0 1283370 0 Name: TARGET, dtype: int64
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, random_state=70)
X_train.head()
| CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT_x | AMT_ANNUITY_x | AMT_GOODS_PRICE_x | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | CNT_FAM_MEMBERS | HOUR_APPR_PROCESS_START_x | LIVE_CITY_NOT_WORK_CITY | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | AMT_ANNUITY_y | AMT_APPLICATION | AMT_CREDIT_y | AMT_GOODS_PRICE_y | HOUR_APPR_PROCESS_START_y | FLAG_LAST_APPL_PER_CONTRACT | NFLAG_LAST_APPL_IN_DAY | DAYS_DECISION | SELLERPLACE_AREA | CNT_PAYMENT | DAYS_FIRST_DRAWING | DAYS_FIRST_DUE | DAYS_LAST_DUE_1ST_VERSION | DAYS_LAST_DUE | DAYS_TERMINATION | FLAG_OWN_CAR_1 | FLAG_OWN_REALTY_1 | FLAG_EMP_PHONE_1 | FLAG_WORK_PHONE_1 | FLAG_CONT_MOBILE_1 | FLAG_PHONE_1 | FLAG_EMAIL_1 | REGION_RATING_CLIENT_2 | REGION_RATING_CLIENT_3 | REGION_RATING_CLIENT_W_CITY_2 | REGION_RATING_CLIENT_W_CITY_3 | REG_REGION_NOT_LIVE_REGION_1 | REG_REGION_NOT_WORK_REGION_1 | LIVE_REGION_NOT_WORK_REGION_1 | REG_CITY_NOT_LIVE_CITY_1 | REG_CITY_NOT_WORK_CITY_1 | FLAG_DOCUMENT_2_1 | FLAG_DOCUMENT_3_1 | FLAG_DOCUMENT_4_1 | FLAG_DOCUMENT_5_1 | FLAG_DOCUMENT_6_1 | FLAG_DOCUMENT_7_1 | FLAG_DOCUMENT_8_1 | FLAG_DOCUMENT_9_1 | FLAG_DOCUMENT_10_1 | FLAG_DOCUMENT_11_1 | FLAG_DOCUMENT_12_1 | FLAG_DOCUMENT_13_1 | FLAG_DOCUMENT_14_1 | FLAG_DOCUMENT_15_1 | FLAG_DOCUMENT_16_1 | FLAG_DOCUMENT_17_1 | FLAG_DOCUMENT_18_1 | FLAG_DOCUMENT_19_1 | FLAG_DOCUMENT_20_1 | FLAG_DOCUMENT_21_1 | NFLAG_INSURED_ON_APPROVAL_1.0 | NAME_CONTRACT_TYPE_x_Revolving loans | CODE_GENDER_M | CODE_GENDER_XNA | NAME_TYPE_SUITE_x_Family | NAME_TYPE_SUITE_x_Group of people | NAME_TYPE_SUITE_x_Other_A | NAME_TYPE_SUITE_x_Other_B | NAME_TYPE_SUITE_x_Spouse, partner | NAME_TYPE_SUITE_x_Unaccompanied | NAME_INCOME_TYPE_Maternity leave | NAME_INCOME_TYPE_Pensioner | NAME_INCOME_TYPE_State servant | NAME_INCOME_TYPE_Student | NAME_INCOME_TYPE_Unemployed | NAME_INCOME_TYPE_Working | NAME_EDUCATION_TYPE_Higher education | NAME_EDUCATION_TYPE_Incomplete higher | NAME_EDUCATION_TYPE_Lower secondary | NAME_EDUCATION_TYPE_Secondary / secondary special | NAME_FAMILY_STATUS_Married | NAME_FAMILY_STATUS_Separated | NAME_FAMILY_STATUS_Single / not married | NAME_FAMILY_STATUS_Widow | NAME_HOUSING_TYPE_House / apartment | NAME_HOUSING_TYPE_Municipal apartment | NAME_HOUSING_TYPE_Office apartment | NAME_HOUSING_TYPE_Rented apartment | NAME_HOUSING_TYPE_With parents | WEEKDAY_APPR_PROCESS_START_x_MONDAY | WEEKDAY_APPR_PROCESS_START_x_SATURDAY | WEEKDAY_APPR_PROCESS_START_x_SUNDAY | WEEKDAY_APPR_PROCESS_START_x_THURSDAY | WEEKDAY_APPR_PROCESS_START_x_TUESDAY | WEEKDAY_APPR_PROCESS_START_x_WEDNESDAY | ORGANIZATION_TYPE_Agriculture | ORGANIZATION_TYPE_Bank | ORGANIZATION_TYPE_Business Entity Type 1 | ORGANIZATION_TYPE_Business Entity Type 2 | ORGANIZATION_TYPE_Business Entity Type 3 | ORGANIZATION_TYPE_Cleaning | ORGANIZATION_TYPE_Construction | ORGANIZATION_TYPE_Culture | ORGANIZATION_TYPE_Electricity | ORGANIZATION_TYPE_Emergency | ORGANIZATION_TYPE_Government | ORGANIZATION_TYPE_Hotel | ORGANIZATION_TYPE_Housing | ORGANIZATION_TYPE_Industry: type 1 | ORGANIZATION_TYPE_Industry: type 10 | ORGANIZATION_TYPE_Industry: type 11 | ORGANIZATION_TYPE_Industry: type 12 | ORGANIZATION_TYPE_Industry: type 13 | ORGANIZATION_TYPE_Industry: type 2 | ORGANIZATION_TYPE_Industry: type 3 | ORGANIZATION_TYPE_Industry: type 4 | ORGANIZATION_TYPE_Industry: type 5 | ORGANIZATION_TYPE_Industry: type 6 | ORGANIZATION_TYPE_Industry: type 7 | ORGANIZATION_TYPE_Industry: type 8 | ORGANIZATION_TYPE_Industry: type 9 | ORGANIZATION_TYPE_Insurance | ORGANIZATION_TYPE_Kindergarten | ORGANIZATION_TYPE_Legal Services | ORGANIZATION_TYPE_Medicine | ORGANIZATION_TYPE_Military | ORGANIZATION_TYPE_Mobile | ORGANIZATION_TYPE_Other | ORGANIZATION_TYPE_Police | ORGANIZATION_TYPE_Postal | ORGANIZATION_TYPE_Realtor | ORGANIZATION_TYPE_Religion | ORGANIZATION_TYPE_Restaurant | ORGANIZATION_TYPE_School | ORGANIZATION_TYPE_Security | ORGANIZATION_TYPE_Security Ministries | ORGANIZATION_TYPE_Self-employed | ORGANIZATION_TYPE_Services | ORGANIZATION_TYPE_Telecom | ORGANIZATION_TYPE_Trade: type 1 | ORGANIZATION_TYPE_Trade: type 2 | ORGANIZATION_TYPE_Trade: type 3 | ORGANIZATION_TYPE_Trade: type 4 | ORGANIZATION_TYPE_Trade: type 5 | ORGANIZATION_TYPE_Trade: type 6 | ORGANIZATION_TYPE_Trade: type 7 | ORGANIZATION_TYPE_Transport: type 1 | ORGANIZATION_TYPE_Transport: type 2 | ORGANIZATION_TYPE_Transport: type 3 | ORGANIZATION_TYPE_Transport: type 4 | ORGANIZATION_TYPE_University | ORGANIZATION_TYPE_XNA | NAME_CONTRACT_TYPE_y_Consumer loans | NAME_CONTRACT_TYPE_y_Revolving loans | WEEKDAY_APPR_PROCESS_START_y_MONDAY | WEEKDAY_APPR_PROCESS_START_y_SATURDAY | WEEKDAY_APPR_PROCESS_START_y_SUNDAY | WEEKDAY_APPR_PROCESS_START_y_THURSDAY | WEEKDAY_APPR_PROCESS_START_y_TUESDAY | WEEKDAY_APPR_PROCESS_START_y_WEDNESDAY | NAME_CASH_LOAN_PURPOSE_Business development | NAME_CASH_LOAN_PURPOSE_Buying a garage | NAME_CASH_LOAN_PURPOSE_Buying a holiday home / land | NAME_CASH_LOAN_PURPOSE_Buying a home | NAME_CASH_LOAN_PURPOSE_Buying a new car | NAME_CASH_LOAN_PURPOSE_Buying a used car | NAME_CASH_LOAN_PURPOSE_Car repairs | NAME_CASH_LOAN_PURPOSE_Education | NAME_CASH_LOAN_PURPOSE_Everyday expenses | NAME_CASH_LOAN_PURPOSE_Furniture | NAME_CASH_LOAN_PURPOSE_Gasification / water supply | NAME_CASH_LOAN_PURPOSE_Hobby | NAME_CASH_LOAN_PURPOSE_Journey | NAME_CASH_LOAN_PURPOSE_Medicine | NAME_CASH_LOAN_PURPOSE_Money for a third person | NAME_CASH_LOAN_PURPOSE_Other | NAME_CASH_LOAN_PURPOSE_Payments on other loans | NAME_CASH_LOAN_PURPOSE_Purchase of electronic equipment | NAME_CASH_LOAN_PURPOSE_Refusal to name the goal | NAME_CASH_LOAN_PURPOSE_Repairs | NAME_CASH_LOAN_PURPOSE_Urgent needs | NAME_CASH_LOAN_PURPOSE_Wedding / gift / holiday | NAME_CASH_LOAN_PURPOSE_XAP | NAME_CASH_LOAN_PURPOSE_XNA | NAME_CONTRACT_STATUS_Canceled | NAME_CONTRACT_STATUS_Refused | NAME_CONTRACT_STATUS_Unused offer | NAME_PAYMENT_TYPE_Cashless from the account of the employer | NAME_PAYMENT_TYPE_Non-cash from your account | NAME_PAYMENT_TYPE_XNA | CODE_REJECT_REASON_HC | CODE_REJECT_REASON_LIMIT | CODE_REJECT_REASON_SCO | CODE_REJECT_REASON_SCOFR | CODE_REJECT_REASON_SYSTEM | CODE_REJECT_REASON_VERIF | CODE_REJECT_REASON_XAP | CODE_REJECT_REASON_XNA | NAME_TYPE_SUITE_y_Family | NAME_TYPE_SUITE_y_Group of people | NAME_TYPE_SUITE_y_Other_A | NAME_TYPE_SUITE_y_Other_B | NAME_TYPE_SUITE_y_Spouse, partner | NAME_TYPE_SUITE_y_Unaccompanied | NAME_CLIENT_TYPE_Refreshed | NAME_CLIENT_TYPE_Repeater | NAME_CLIENT_TYPE_XNA | NAME_GOODS_CATEGORY_Animals | NAME_GOODS_CATEGORY_Audio/Video | NAME_GOODS_CATEGORY_Auto Accessories | NAME_GOODS_CATEGORY_Clothing and Accessories | NAME_GOODS_CATEGORY_Computers | NAME_GOODS_CATEGORY_Construction Materials | NAME_GOODS_CATEGORY_Consumer Electronics | NAME_GOODS_CATEGORY_Direct Sales | NAME_GOODS_CATEGORY_Education | NAME_GOODS_CATEGORY_Fitness | NAME_GOODS_CATEGORY_Furniture | NAME_GOODS_CATEGORY_Gardening | NAME_GOODS_CATEGORY_Homewares | NAME_GOODS_CATEGORY_Insurance | NAME_GOODS_CATEGORY_Jewelry | NAME_GOODS_CATEGORY_Medical Supplies | NAME_GOODS_CATEGORY_Medicine | NAME_GOODS_CATEGORY_Mobile | NAME_GOODS_CATEGORY_Office Appliances | NAME_GOODS_CATEGORY_Other | NAME_GOODS_CATEGORY_Photo / Cinema Equipment | NAME_GOODS_CATEGORY_Sport and Leisure | NAME_GOODS_CATEGORY_Tourism | NAME_GOODS_CATEGORY_Vehicles | NAME_GOODS_CATEGORY_Weapon | NAME_GOODS_CATEGORY_XNA | NAME_PORTFOLIO_Cars | NAME_PORTFOLIO_Cash | NAME_PORTFOLIO_POS | NAME_PORTFOLIO_XNA | NAME_PRODUCT_TYPE_walk-in | NAME_PRODUCT_TYPE_x-sell | CHANNEL_TYPE_Car dealer | CHANNEL_TYPE_Channel of corporate sales | CHANNEL_TYPE_Contact center | CHANNEL_TYPE_Country-wide | CHANNEL_TYPE_Credit and cash offices | CHANNEL_TYPE_Regional / Local | CHANNEL_TYPE_Stone | NAME_SELLER_INDUSTRY_Clothing | NAME_SELLER_INDUSTRY_Connectivity | NAME_SELLER_INDUSTRY_Construction | NAME_SELLER_INDUSTRY_Consumer electronics | NAME_SELLER_INDUSTRY_Furniture | NAME_SELLER_INDUSTRY_Industry | NAME_SELLER_INDUSTRY_Jewelry | NAME_SELLER_INDUSTRY_MLM partners | NAME_SELLER_INDUSTRY_Tourism | NAME_SELLER_INDUSTRY_XNA | NAME_YIELD_GROUP_high | NAME_YIELD_GROUP_low_action | NAME_YIELD_GROUP_low_normal | NAME_YIELD_GROUP_middle | PRODUCT_COMBINATION_Card X-Sell | PRODUCT_COMBINATION_Cash | PRODUCT_COMBINATION_Cash Street: high | PRODUCT_COMBINATION_Cash Street: low | PRODUCT_COMBINATION_Cash Street: middle | PRODUCT_COMBINATION_Cash X-Sell: high | PRODUCT_COMBINATION_Cash X-Sell: low | PRODUCT_COMBINATION_Cash X-Sell: middle | PRODUCT_COMBINATION_POS household with interest | PRODUCT_COMBINATION_POS household without interest | PRODUCT_COMBINATION_POS industry with interest | PRODUCT_COMBINATION_POS industry without interest | PRODUCT_COMBINATION_POS mobile with interest | PRODUCT_COMBINATION_POS mobile without interest | PRODUCT_COMBINATION_POS other with interest | PRODUCT_COMBINATION_POS others without interest | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 886912 | 0 | 225000.0 | 540000.0 | 30280.5 | 540000.0 | 0.035792 | 16136 | -4914 | -8152.0 | 4305 | 2.0 | 19 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 611.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 7.0 | 15837.184952 | 0.0 | 0.0 | 226451.191283 | 14 | 1 | 1 | -254 | -1 | 12.0 | 365243.0 | -825.0 | -358.0 | -534.0 | -494.0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1345274 | 0 | 225000.0 | 387000.0 | 14719.5 | 387000.0 | 0.018209 | 21455 | -3406 | -12828.0 | 4196 | 2.0 | 15 | 0 | 4.0 | 0.0 | 4.0 | 0.0 | 1067.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 | 15837.184952 | 0.0 | 0.0 | 226451.191283 | 9 | 1 | 1 | -27 | -1 | 12.0 | 365243.0 | -825.0 | -358.0 | -534.0 | -494.0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 710187 | 0 | 202500.0 | 157500.0 | 7875.0 | 157500.0 | 0.019101 | 20914 | -1229 | -1980.0 | 1985 | 2.0 | 17 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 985.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 15837.184952 | 0.0 | 0.0 | 226451.191283 | 11 | 1 | 1 | -19 | -1 | 12.0 | 365243.0 | -825.0 | -358.0 | -534.0 | -494.0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 969891 | 0 | 112500.0 | 450000.0 | 24543.0 | 450000.0 | 0.028663 | 20557 | 365243 | -23.0 | 3445 | 1.0 | 14 | 0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 9131.760000 | 70267.5 | 68454.0 | 70267.500000 | 14 | 1 | 1 | -2678 | 80 | 10.0 | 365243.0 | -2647.0 | -2377.0 | -2377.0 | -2370.0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 1169924 | 0 | 225000.0 | 1408806.0 | 92457.0 | 1350000.0 | 0.031329 | 18667 | -564 | -5628.0 | 2207 | 2.0 | 10 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 2782.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6621.795000 | 47533.5 | 23242.5 | 47533.500000 | 12 | 1 | 1 | -2782 | 30 | 4.0 | 365243.0 | -2751.0 | -2661.0 | -2661.0 | -2609.0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
X_train.shape
(4900, 291)
X_test.head()
| CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT_x | AMT_ANNUITY_x | AMT_GOODS_PRICE_x | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | CNT_FAM_MEMBERS | HOUR_APPR_PROCESS_START_x | LIVE_CITY_NOT_WORK_CITY | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | AMT_ANNUITY_y | AMT_APPLICATION | AMT_CREDIT_y | AMT_GOODS_PRICE_y | HOUR_APPR_PROCESS_START_y | FLAG_LAST_APPL_PER_CONTRACT | NFLAG_LAST_APPL_IN_DAY | DAYS_DECISION | SELLERPLACE_AREA | CNT_PAYMENT | DAYS_FIRST_DRAWING | DAYS_FIRST_DUE | DAYS_LAST_DUE_1ST_VERSION | DAYS_LAST_DUE | DAYS_TERMINATION | FLAG_OWN_CAR_1 | FLAG_OWN_REALTY_1 | FLAG_EMP_PHONE_1 | FLAG_WORK_PHONE_1 | FLAG_CONT_MOBILE_1 | FLAG_PHONE_1 | FLAG_EMAIL_1 | REGION_RATING_CLIENT_2 | REGION_RATING_CLIENT_3 | REGION_RATING_CLIENT_W_CITY_2 | REGION_RATING_CLIENT_W_CITY_3 | REG_REGION_NOT_LIVE_REGION_1 | REG_REGION_NOT_WORK_REGION_1 | LIVE_REGION_NOT_WORK_REGION_1 | REG_CITY_NOT_LIVE_CITY_1 | REG_CITY_NOT_WORK_CITY_1 | FLAG_DOCUMENT_2_1 | FLAG_DOCUMENT_3_1 | FLAG_DOCUMENT_4_1 | FLAG_DOCUMENT_5_1 | FLAG_DOCUMENT_6_1 | FLAG_DOCUMENT_7_1 | FLAG_DOCUMENT_8_1 | FLAG_DOCUMENT_9_1 | FLAG_DOCUMENT_10_1 | FLAG_DOCUMENT_11_1 | FLAG_DOCUMENT_12_1 | FLAG_DOCUMENT_13_1 | FLAG_DOCUMENT_14_1 | FLAG_DOCUMENT_15_1 | FLAG_DOCUMENT_16_1 | FLAG_DOCUMENT_17_1 | FLAG_DOCUMENT_18_1 | FLAG_DOCUMENT_19_1 | FLAG_DOCUMENT_20_1 | FLAG_DOCUMENT_21_1 | NFLAG_INSURED_ON_APPROVAL_1.0 | NAME_CONTRACT_TYPE_x_Revolving loans | CODE_GENDER_M | CODE_GENDER_XNA | NAME_TYPE_SUITE_x_Family | NAME_TYPE_SUITE_x_Group of people | NAME_TYPE_SUITE_x_Other_A | NAME_TYPE_SUITE_x_Other_B | NAME_TYPE_SUITE_x_Spouse, partner | NAME_TYPE_SUITE_x_Unaccompanied | NAME_INCOME_TYPE_Maternity leave | NAME_INCOME_TYPE_Pensioner | NAME_INCOME_TYPE_State servant | NAME_INCOME_TYPE_Student | NAME_INCOME_TYPE_Unemployed | NAME_INCOME_TYPE_Working | NAME_EDUCATION_TYPE_Higher education | NAME_EDUCATION_TYPE_Incomplete higher | NAME_EDUCATION_TYPE_Lower secondary | NAME_EDUCATION_TYPE_Secondary / secondary special | NAME_FAMILY_STATUS_Married | NAME_FAMILY_STATUS_Separated | NAME_FAMILY_STATUS_Single / not married | NAME_FAMILY_STATUS_Widow | NAME_HOUSING_TYPE_House / apartment | NAME_HOUSING_TYPE_Municipal apartment | NAME_HOUSING_TYPE_Office apartment | NAME_HOUSING_TYPE_Rented apartment | NAME_HOUSING_TYPE_With parents | WEEKDAY_APPR_PROCESS_START_x_MONDAY | WEEKDAY_APPR_PROCESS_START_x_SATURDAY | WEEKDAY_APPR_PROCESS_START_x_SUNDAY | WEEKDAY_APPR_PROCESS_START_x_THURSDAY | WEEKDAY_APPR_PROCESS_START_x_TUESDAY | WEEKDAY_APPR_PROCESS_START_x_WEDNESDAY | ORGANIZATION_TYPE_Agriculture | ORGANIZATION_TYPE_Bank | ORGANIZATION_TYPE_Business Entity Type 1 | ORGANIZATION_TYPE_Business Entity Type 2 | ORGANIZATION_TYPE_Business Entity Type 3 | ORGANIZATION_TYPE_Cleaning | ORGANIZATION_TYPE_Construction | ORGANIZATION_TYPE_Culture | ORGANIZATION_TYPE_Electricity | ORGANIZATION_TYPE_Emergency | ORGANIZATION_TYPE_Government | ORGANIZATION_TYPE_Hotel | ORGANIZATION_TYPE_Housing | ORGANIZATION_TYPE_Industry: type 1 | ORGANIZATION_TYPE_Industry: type 10 | ORGANIZATION_TYPE_Industry: type 11 | ORGANIZATION_TYPE_Industry: type 12 | ORGANIZATION_TYPE_Industry: type 13 | ORGANIZATION_TYPE_Industry: type 2 | ORGANIZATION_TYPE_Industry: type 3 | ORGANIZATION_TYPE_Industry: type 4 | ORGANIZATION_TYPE_Industry: type 5 | ORGANIZATION_TYPE_Industry: type 6 | ORGANIZATION_TYPE_Industry: type 7 | ORGANIZATION_TYPE_Industry: type 8 | ORGANIZATION_TYPE_Industry: type 9 | ORGANIZATION_TYPE_Insurance | ORGANIZATION_TYPE_Kindergarten | ORGANIZATION_TYPE_Legal Services | ORGANIZATION_TYPE_Medicine | ORGANIZATION_TYPE_Military | ORGANIZATION_TYPE_Mobile | ORGANIZATION_TYPE_Other | ORGANIZATION_TYPE_Police | ORGANIZATION_TYPE_Postal | ORGANIZATION_TYPE_Realtor | ORGANIZATION_TYPE_Religion | ORGANIZATION_TYPE_Restaurant | ORGANIZATION_TYPE_School | ORGANIZATION_TYPE_Security | ORGANIZATION_TYPE_Security Ministries | ORGANIZATION_TYPE_Self-employed | ORGANIZATION_TYPE_Services | ORGANIZATION_TYPE_Telecom | ORGANIZATION_TYPE_Trade: type 1 | ORGANIZATION_TYPE_Trade: type 2 | ORGANIZATION_TYPE_Trade: type 3 | ORGANIZATION_TYPE_Trade: type 4 | ORGANIZATION_TYPE_Trade: type 5 | ORGANIZATION_TYPE_Trade: type 6 | ORGANIZATION_TYPE_Trade: type 7 | ORGANIZATION_TYPE_Transport: type 1 | ORGANIZATION_TYPE_Transport: type 2 | ORGANIZATION_TYPE_Transport: type 3 | ORGANIZATION_TYPE_Transport: type 4 | ORGANIZATION_TYPE_University | ORGANIZATION_TYPE_XNA | NAME_CONTRACT_TYPE_y_Consumer loans | NAME_CONTRACT_TYPE_y_Revolving loans | WEEKDAY_APPR_PROCESS_START_y_MONDAY | WEEKDAY_APPR_PROCESS_START_y_SATURDAY | WEEKDAY_APPR_PROCESS_START_y_SUNDAY | WEEKDAY_APPR_PROCESS_START_y_THURSDAY | WEEKDAY_APPR_PROCESS_START_y_TUESDAY | WEEKDAY_APPR_PROCESS_START_y_WEDNESDAY | NAME_CASH_LOAN_PURPOSE_Business development | NAME_CASH_LOAN_PURPOSE_Buying a garage | NAME_CASH_LOAN_PURPOSE_Buying a holiday home / land | NAME_CASH_LOAN_PURPOSE_Buying a home | NAME_CASH_LOAN_PURPOSE_Buying a new car | NAME_CASH_LOAN_PURPOSE_Buying a used car | NAME_CASH_LOAN_PURPOSE_Car repairs | NAME_CASH_LOAN_PURPOSE_Education | NAME_CASH_LOAN_PURPOSE_Everyday expenses | NAME_CASH_LOAN_PURPOSE_Furniture | NAME_CASH_LOAN_PURPOSE_Gasification / water supply | NAME_CASH_LOAN_PURPOSE_Hobby | NAME_CASH_LOAN_PURPOSE_Journey | NAME_CASH_LOAN_PURPOSE_Medicine | NAME_CASH_LOAN_PURPOSE_Money for a third person | NAME_CASH_LOAN_PURPOSE_Other | NAME_CASH_LOAN_PURPOSE_Payments on other loans | NAME_CASH_LOAN_PURPOSE_Purchase of electronic equipment | NAME_CASH_LOAN_PURPOSE_Refusal to name the goal | NAME_CASH_LOAN_PURPOSE_Repairs | NAME_CASH_LOAN_PURPOSE_Urgent needs | NAME_CASH_LOAN_PURPOSE_Wedding / gift / holiday | NAME_CASH_LOAN_PURPOSE_XAP | NAME_CASH_LOAN_PURPOSE_XNA | NAME_CONTRACT_STATUS_Canceled | NAME_CONTRACT_STATUS_Refused | NAME_CONTRACT_STATUS_Unused offer | NAME_PAYMENT_TYPE_Cashless from the account of the employer | NAME_PAYMENT_TYPE_Non-cash from your account | NAME_PAYMENT_TYPE_XNA | CODE_REJECT_REASON_HC | CODE_REJECT_REASON_LIMIT | CODE_REJECT_REASON_SCO | CODE_REJECT_REASON_SCOFR | CODE_REJECT_REASON_SYSTEM | CODE_REJECT_REASON_VERIF | CODE_REJECT_REASON_XAP | CODE_REJECT_REASON_XNA | NAME_TYPE_SUITE_y_Family | NAME_TYPE_SUITE_y_Group of people | NAME_TYPE_SUITE_y_Other_A | NAME_TYPE_SUITE_y_Other_B | NAME_TYPE_SUITE_y_Spouse, partner | NAME_TYPE_SUITE_y_Unaccompanied | NAME_CLIENT_TYPE_Refreshed | NAME_CLIENT_TYPE_Repeater | NAME_CLIENT_TYPE_XNA | NAME_GOODS_CATEGORY_Animals | NAME_GOODS_CATEGORY_Audio/Video | NAME_GOODS_CATEGORY_Auto Accessories | NAME_GOODS_CATEGORY_Clothing and Accessories | NAME_GOODS_CATEGORY_Computers | NAME_GOODS_CATEGORY_Construction Materials | NAME_GOODS_CATEGORY_Consumer Electronics | NAME_GOODS_CATEGORY_Direct Sales | NAME_GOODS_CATEGORY_Education | NAME_GOODS_CATEGORY_Fitness | NAME_GOODS_CATEGORY_Furniture | NAME_GOODS_CATEGORY_Gardening | NAME_GOODS_CATEGORY_Homewares | NAME_GOODS_CATEGORY_Insurance | NAME_GOODS_CATEGORY_Jewelry | NAME_GOODS_CATEGORY_Medical Supplies | NAME_GOODS_CATEGORY_Medicine | NAME_GOODS_CATEGORY_Mobile | NAME_GOODS_CATEGORY_Office Appliances | NAME_GOODS_CATEGORY_Other | NAME_GOODS_CATEGORY_Photo / Cinema Equipment | NAME_GOODS_CATEGORY_Sport and Leisure | NAME_GOODS_CATEGORY_Tourism | NAME_GOODS_CATEGORY_Vehicles | NAME_GOODS_CATEGORY_Weapon | NAME_GOODS_CATEGORY_XNA | NAME_PORTFOLIO_Cars | NAME_PORTFOLIO_Cash | NAME_PORTFOLIO_POS | NAME_PORTFOLIO_XNA | NAME_PRODUCT_TYPE_walk-in | NAME_PRODUCT_TYPE_x-sell | CHANNEL_TYPE_Car dealer | CHANNEL_TYPE_Channel of corporate sales | CHANNEL_TYPE_Contact center | CHANNEL_TYPE_Country-wide | CHANNEL_TYPE_Credit and cash offices | CHANNEL_TYPE_Regional / Local | CHANNEL_TYPE_Stone | NAME_SELLER_INDUSTRY_Clothing | NAME_SELLER_INDUSTRY_Connectivity | NAME_SELLER_INDUSTRY_Construction | NAME_SELLER_INDUSTRY_Consumer electronics | NAME_SELLER_INDUSTRY_Furniture | NAME_SELLER_INDUSTRY_Industry | NAME_SELLER_INDUSTRY_Jewelry | NAME_SELLER_INDUSTRY_MLM partners | NAME_SELLER_INDUSTRY_Tourism | NAME_SELLER_INDUSTRY_XNA | NAME_YIELD_GROUP_high | NAME_YIELD_GROUP_low_action | NAME_YIELD_GROUP_low_normal | NAME_YIELD_GROUP_middle | PRODUCT_COMBINATION_Card X-Sell | PRODUCT_COMBINATION_Cash | PRODUCT_COMBINATION_Cash Street: high | PRODUCT_COMBINATION_Cash Street: low | PRODUCT_COMBINATION_Cash Street: middle | PRODUCT_COMBINATION_Cash X-Sell: high | PRODUCT_COMBINATION_Cash X-Sell: low | PRODUCT_COMBINATION_Cash X-Sell: middle | PRODUCT_COMBINATION_POS household with interest | PRODUCT_COMBINATION_POS household without interest | PRODUCT_COMBINATION_POS industry with interest | PRODUCT_COMBINATION_POS industry without interest | PRODUCT_COMBINATION_POS mobile with interest | PRODUCT_COMBINATION_POS mobile without interest | PRODUCT_COMBINATION_POS other with interest | PRODUCT_COMBINATION_POS others without interest | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 563940 | 0 | 202500.0 | 1024740.0 | 52452.0 | 900000.0 | 0.007114 | 19631 | 365243 | -174.0 | 3177 | 2.0 | 13 | 0 | 2.0 | 0.0 | 2.0 | 0.0 | 159.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 8.0 | 15837.184952 | 0.0 | 0.0 | 226451.191283 | 12 | 1 | 1 | -169 | -1 | 12.0 | 365243.0 | -825.0 | -358.0 | -534.0 | -494.0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 186208 | 0 | 112500.0 | 468733.5 | 21982.5 | 387000.0 | 0.022800 | 19606 | -336 | -3935.0 | 3085 | 2.0 | 9 | 1 | 1.0 | 1.0 | 1.0 | 1.0 | 1815.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 15837.184952 | 133065.0 | 133065.0 | 133065.000000 | 9 | 1 | 1 | -1815 | 1882 | 12.0 | 365243.0 | -825.0 | -358.0 | -534.0 | -494.0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 739774 | 0 | 270000.0 | 566055.0 | 18387.0 | 472500.0 | 0.018801 | 21797 | -6903 | -6437.0 | 4015 | 2.0 | 14 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1495.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 1.0 | 13481.955000 | 174411.0 | 174411.0 | 174411.000000 | 17 | 1 | 1 | -734 | 15 | 14.0 | 365243.0 | -700.0 | -310.0 | -490.0 | -484.0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 767186 | 0 | 90000.0 | 175500.0 | 15192.0 | 175500.0 | 0.014520 | 21014 | 365243 | -3963.0 | 3963 | 2.0 | 9 | 0 | 1.0 | 0.0 | 1.0 | 0.0 | 601.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 4.0 | 17607.240000 | 121941.0 | 95215.5 | 121941.000000 | 13 | 1 | 1 | -601 | 2500 | 6.0 | 365243.0 | -570.0 | -420.0 | -450.0 | -441.0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 502320 | 1 | 157500.0 | 492862.5 | 24102.0 | 346500.0 | 0.018801 | 13246 | -1565 | -7112.0 | 4909 | 3.0 | 6 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | 8335.980000 | 229500.0 | 265477.5 | 229500.000000 | 7 | 1 | 1 | -252 | -1 | 48.0 | 365243.0 | -222.0 | 1188.0 | 365243.0 | 365243.0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
X_test.shape
(2100, 291)
y_train.head()
886912 0 1345274 0 710187 0 969891 0 1169924 0 Name: TARGET, dtype: int64
y_train.shape
(4900,)
y_test.head()
563940 0 186208 0 739774 0 767186 0 502320 0 Name: TARGET, dtype: int64
y_test.shape
(2100,)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train[['CNT_CHILDREN','AMT_INCOME_TOTAL','AMT_CREDIT_x','AMT_ANNUITY_x',
'AMT_GOODS_PRICE_x','REGION_POPULATION_RELATIVE','DAYS_BIRTH',
'DAYS_EMPLOYED','DAYS_REGISTRATION','DAYS_ID_PUBLISH','CNT_FAM_MEMBERS',
'HOUR_APPR_PROCESS_START_x','LIVE_CITY_NOT_WORK_CITY','OBS_30_CNT_SOCIAL_CIRCLE',
'DEF_30_CNT_SOCIAL_CIRCLE','OBS_60_CNT_SOCIAL_CIRCLE','DEF_60_CNT_SOCIAL_CIRCLE',
'DAYS_LAST_PHONE_CHANGE','AMT_REQ_CREDIT_BUREAU_HOUR','AMT_REQ_CREDIT_BUREAU_DAY',
'AMT_REQ_CREDIT_BUREAU_WEEK','AMT_REQ_CREDIT_BUREAU_MON','AMT_REQ_CREDIT_BUREAU_QRT',
'AMT_REQ_CREDIT_BUREAU_YEAR','AMT_ANNUITY_y','AMT_APPLICATION','AMT_CREDIT_y',
'AMT_GOODS_PRICE_y','HOUR_APPR_PROCESS_START_y','FLAG_LAST_APPL_PER_CONTRACT',
'NFLAG_LAST_APPL_IN_DAY','DAYS_DECISION','SELLERPLACE_AREA','CNT_PAYMENT',
'DAYS_FIRST_DRAWING','DAYS_FIRST_DUE','DAYS_LAST_DUE_1ST_VERSION','DAYS_LAST_DUE',
'DAYS_TERMINATION']] = scaler.fit_transform(X_train[['CNT_CHILDREN','AMT_INCOME_TOTAL','AMT_CREDIT_x',
'AMT_ANNUITY_x','AMT_GOODS_PRICE_x','REGION_POPULATION_RELATIVE',
'DAYS_BIRTH','DAYS_EMPLOYED','DAYS_REGISTRATION','DAYS_ID_PUBLISH',
'CNT_FAM_MEMBERS','HOUR_APPR_PROCESS_START_x','LIVE_CITY_NOT_WORK_CITY',
'OBS_30_CNT_SOCIAL_CIRCLE','DEF_30_CNT_SOCIAL_CIRCLE','OBS_60_CNT_SOCIAL_CIRCLE',
'DEF_60_CNT_SOCIAL_CIRCLE','DAYS_LAST_PHONE_CHANGE','AMT_REQ_CREDIT_BUREAU_HOUR',
'AMT_REQ_CREDIT_BUREAU_DAY','AMT_REQ_CREDIT_BUREAU_WEEK','AMT_REQ_CREDIT_BUREAU_MON',
'AMT_REQ_CREDIT_BUREAU_QRT','AMT_REQ_CREDIT_BUREAU_YEAR','AMT_ANNUITY_y','AMT_APPLICATION',
'AMT_CREDIT_y','AMT_GOODS_PRICE_y','HOUR_APPR_PROCESS_START_y','FLAG_LAST_APPL_PER_CONTRACT',
'NFLAG_LAST_APPL_IN_DAY','DAYS_DECISION','SELLERPLACE_AREA','CNT_PAYMENT','DAYS_FIRST_DRAWING',
'DAYS_FIRST_DUE','DAYS_LAST_DUE_1ST_VERSION','DAYS_LAST_DUE','DAYS_TERMINATION']])
X_train.head()
| CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT_x | AMT_ANNUITY_x | AMT_GOODS_PRICE_x | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | CNT_FAM_MEMBERS | HOUR_APPR_PROCESS_START_x | LIVE_CITY_NOT_WORK_CITY | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | AMT_ANNUITY_y | AMT_APPLICATION | AMT_CREDIT_y | AMT_GOODS_PRICE_y | HOUR_APPR_PROCESS_START_y | FLAG_LAST_APPL_PER_CONTRACT | NFLAG_LAST_APPL_IN_DAY | DAYS_DECISION | SELLERPLACE_AREA | CNT_PAYMENT | DAYS_FIRST_DRAWING | DAYS_FIRST_DUE | DAYS_LAST_DUE_1ST_VERSION | DAYS_LAST_DUE | DAYS_TERMINATION | FLAG_OWN_CAR_1 | FLAG_OWN_REALTY_1 | FLAG_EMP_PHONE_1 | FLAG_WORK_PHONE_1 | FLAG_CONT_MOBILE_1 | FLAG_PHONE_1 | FLAG_EMAIL_1 | REGION_RATING_CLIENT_2 | REGION_RATING_CLIENT_3 | REGION_RATING_CLIENT_W_CITY_2 | REGION_RATING_CLIENT_W_CITY_3 | REG_REGION_NOT_LIVE_REGION_1 | REG_REGION_NOT_WORK_REGION_1 | LIVE_REGION_NOT_WORK_REGION_1 | REG_CITY_NOT_LIVE_CITY_1 | REG_CITY_NOT_WORK_CITY_1 | FLAG_DOCUMENT_2_1 | FLAG_DOCUMENT_3_1 | FLAG_DOCUMENT_4_1 | FLAG_DOCUMENT_5_1 | FLAG_DOCUMENT_6_1 | FLAG_DOCUMENT_7_1 | FLAG_DOCUMENT_8_1 | FLAG_DOCUMENT_9_1 | FLAG_DOCUMENT_10_1 | FLAG_DOCUMENT_11_1 | FLAG_DOCUMENT_12_1 | FLAG_DOCUMENT_13_1 | FLAG_DOCUMENT_14_1 | FLAG_DOCUMENT_15_1 | FLAG_DOCUMENT_16_1 | FLAG_DOCUMENT_17_1 | FLAG_DOCUMENT_18_1 | FLAG_DOCUMENT_19_1 | FLAG_DOCUMENT_20_1 | FLAG_DOCUMENT_21_1 | NFLAG_INSURED_ON_APPROVAL_1.0 | NAME_CONTRACT_TYPE_x_Revolving loans | CODE_GENDER_M | CODE_GENDER_XNA | NAME_TYPE_SUITE_x_Family | NAME_TYPE_SUITE_x_Group of people | NAME_TYPE_SUITE_x_Other_A | NAME_TYPE_SUITE_x_Other_B | NAME_TYPE_SUITE_x_Spouse, partner | NAME_TYPE_SUITE_x_Unaccompanied | NAME_INCOME_TYPE_Maternity leave | NAME_INCOME_TYPE_Pensioner | NAME_INCOME_TYPE_State servant | NAME_INCOME_TYPE_Student | NAME_INCOME_TYPE_Unemployed | NAME_INCOME_TYPE_Working | NAME_EDUCATION_TYPE_Higher education | NAME_EDUCATION_TYPE_Incomplete higher | NAME_EDUCATION_TYPE_Lower secondary | NAME_EDUCATION_TYPE_Secondary / secondary special | NAME_FAMILY_STATUS_Married | NAME_FAMILY_STATUS_Separated | NAME_FAMILY_STATUS_Single / not married | NAME_FAMILY_STATUS_Widow | NAME_HOUSING_TYPE_House / apartment | NAME_HOUSING_TYPE_Municipal apartment | NAME_HOUSING_TYPE_Office apartment | NAME_HOUSING_TYPE_Rented apartment | NAME_HOUSING_TYPE_With parents | WEEKDAY_APPR_PROCESS_START_x_MONDAY | WEEKDAY_APPR_PROCESS_START_x_SATURDAY | WEEKDAY_APPR_PROCESS_START_x_SUNDAY | WEEKDAY_APPR_PROCESS_START_x_THURSDAY | WEEKDAY_APPR_PROCESS_START_x_TUESDAY | WEEKDAY_APPR_PROCESS_START_x_WEDNESDAY | ORGANIZATION_TYPE_Agriculture | ORGANIZATION_TYPE_Bank | ORGANIZATION_TYPE_Business Entity Type 1 | ORGANIZATION_TYPE_Business Entity Type 2 | ORGANIZATION_TYPE_Business Entity Type 3 | ORGANIZATION_TYPE_Cleaning | ORGANIZATION_TYPE_Construction | ORGANIZATION_TYPE_Culture | ORGANIZATION_TYPE_Electricity | ORGANIZATION_TYPE_Emergency | ORGANIZATION_TYPE_Government | ORGANIZATION_TYPE_Hotel | ORGANIZATION_TYPE_Housing | ORGANIZATION_TYPE_Industry: type 1 | ORGANIZATION_TYPE_Industry: type 10 | ORGANIZATION_TYPE_Industry: type 11 | ORGANIZATION_TYPE_Industry: type 12 | ORGANIZATION_TYPE_Industry: type 13 | ORGANIZATION_TYPE_Industry: type 2 | ORGANIZATION_TYPE_Industry: type 3 | ORGANIZATION_TYPE_Industry: type 4 | ORGANIZATION_TYPE_Industry: type 5 | ORGANIZATION_TYPE_Industry: type 6 | ORGANIZATION_TYPE_Industry: type 7 | ORGANIZATION_TYPE_Industry: type 8 | ORGANIZATION_TYPE_Industry: type 9 | ORGANIZATION_TYPE_Insurance | ORGANIZATION_TYPE_Kindergarten | ORGANIZATION_TYPE_Legal Services | ORGANIZATION_TYPE_Medicine | ORGANIZATION_TYPE_Military | ORGANIZATION_TYPE_Mobile | ORGANIZATION_TYPE_Other | ORGANIZATION_TYPE_Police | ORGANIZATION_TYPE_Postal | ORGANIZATION_TYPE_Realtor | ORGANIZATION_TYPE_Religion | ORGANIZATION_TYPE_Restaurant | ORGANIZATION_TYPE_School | ORGANIZATION_TYPE_Security | ORGANIZATION_TYPE_Security Ministries | ORGANIZATION_TYPE_Self-employed | ORGANIZATION_TYPE_Services | ORGANIZATION_TYPE_Telecom | ORGANIZATION_TYPE_Trade: type 1 | ORGANIZATION_TYPE_Trade: type 2 | ORGANIZATION_TYPE_Trade: type 3 | ORGANIZATION_TYPE_Trade: type 4 | ORGANIZATION_TYPE_Trade: type 5 | ORGANIZATION_TYPE_Trade: type 6 | ORGANIZATION_TYPE_Trade: type 7 | ORGANIZATION_TYPE_Transport: type 1 | ORGANIZATION_TYPE_Transport: type 2 | ORGANIZATION_TYPE_Transport: type 3 | ORGANIZATION_TYPE_Transport: type 4 | ORGANIZATION_TYPE_University | ORGANIZATION_TYPE_XNA | NAME_CONTRACT_TYPE_y_Consumer loans | NAME_CONTRACT_TYPE_y_Revolving loans | WEEKDAY_APPR_PROCESS_START_y_MONDAY | WEEKDAY_APPR_PROCESS_START_y_SATURDAY | WEEKDAY_APPR_PROCESS_START_y_SUNDAY | WEEKDAY_APPR_PROCESS_START_y_THURSDAY | WEEKDAY_APPR_PROCESS_START_y_TUESDAY | WEEKDAY_APPR_PROCESS_START_y_WEDNESDAY | NAME_CASH_LOAN_PURPOSE_Business development | NAME_CASH_LOAN_PURPOSE_Buying a garage | NAME_CASH_LOAN_PURPOSE_Buying a holiday home / land | NAME_CASH_LOAN_PURPOSE_Buying a home | NAME_CASH_LOAN_PURPOSE_Buying a new car | NAME_CASH_LOAN_PURPOSE_Buying a used car | NAME_CASH_LOAN_PURPOSE_Car repairs | NAME_CASH_LOAN_PURPOSE_Education | NAME_CASH_LOAN_PURPOSE_Everyday expenses | NAME_CASH_LOAN_PURPOSE_Furniture | NAME_CASH_LOAN_PURPOSE_Gasification / water supply | NAME_CASH_LOAN_PURPOSE_Hobby | NAME_CASH_LOAN_PURPOSE_Journey | NAME_CASH_LOAN_PURPOSE_Medicine | NAME_CASH_LOAN_PURPOSE_Money for a third person | NAME_CASH_LOAN_PURPOSE_Other | NAME_CASH_LOAN_PURPOSE_Payments on other loans | NAME_CASH_LOAN_PURPOSE_Purchase of electronic equipment | NAME_CASH_LOAN_PURPOSE_Refusal to name the goal | NAME_CASH_LOAN_PURPOSE_Repairs | NAME_CASH_LOAN_PURPOSE_Urgent needs | NAME_CASH_LOAN_PURPOSE_Wedding / gift / holiday | NAME_CASH_LOAN_PURPOSE_XAP | NAME_CASH_LOAN_PURPOSE_XNA | NAME_CONTRACT_STATUS_Canceled | NAME_CONTRACT_STATUS_Refused | NAME_CONTRACT_STATUS_Unused offer | NAME_PAYMENT_TYPE_Cashless from the account of the employer | NAME_PAYMENT_TYPE_Non-cash from your account | NAME_PAYMENT_TYPE_XNA | CODE_REJECT_REASON_HC | CODE_REJECT_REASON_LIMIT | CODE_REJECT_REASON_SCO | CODE_REJECT_REASON_SCOFR | CODE_REJECT_REASON_SYSTEM | CODE_REJECT_REASON_VERIF | CODE_REJECT_REASON_XAP | CODE_REJECT_REASON_XNA | NAME_TYPE_SUITE_y_Family | NAME_TYPE_SUITE_y_Group of people | NAME_TYPE_SUITE_y_Other_A | NAME_TYPE_SUITE_y_Other_B | NAME_TYPE_SUITE_y_Spouse, partner | NAME_TYPE_SUITE_y_Unaccompanied | NAME_CLIENT_TYPE_Refreshed | NAME_CLIENT_TYPE_Repeater | NAME_CLIENT_TYPE_XNA | NAME_GOODS_CATEGORY_Animals | NAME_GOODS_CATEGORY_Audio/Video | NAME_GOODS_CATEGORY_Auto Accessories | NAME_GOODS_CATEGORY_Clothing and Accessories | NAME_GOODS_CATEGORY_Computers | NAME_GOODS_CATEGORY_Construction Materials | NAME_GOODS_CATEGORY_Consumer Electronics | NAME_GOODS_CATEGORY_Direct Sales | NAME_GOODS_CATEGORY_Education | NAME_GOODS_CATEGORY_Fitness | NAME_GOODS_CATEGORY_Furniture | NAME_GOODS_CATEGORY_Gardening | NAME_GOODS_CATEGORY_Homewares | NAME_GOODS_CATEGORY_Insurance | NAME_GOODS_CATEGORY_Jewelry | NAME_GOODS_CATEGORY_Medical Supplies | NAME_GOODS_CATEGORY_Medicine | NAME_GOODS_CATEGORY_Mobile | NAME_GOODS_CATEGORY_Office Appliances | NAME_GOODS_CATEGORY_Other | NAME_GOODS_CATEGORY_Photo / Cinema Equipment | NAME_GOODS_CATEGORY_Sport and Leisure | NAME_GOODS_CATEGORY_Tourism | NAME_GOODS_CATEGORY_Vehicles | NAME_GOODS_CATEGORY_Weapon | NAME_GOODS_CATEGORY_XNA | NAME_PORTFOLIO_Cars | NAME_PORTFOLIO_Cash | NAME_PORTFOLIO_POS | NAME_PORTFOLIO_XNA | NAME_PRODUCT_TYPE_walk-in | NAME_PRODUCT_TYPE_x-sell | CHANNEL_TYPE_Car dealer | CHANNEL_TYPE_Channel of corporate sales | CHANNEL_TYPE_Contact center | CHANNEL_TYPE_Country-wide | CHANNEL_TYPE_Credit and cash offices | CHANNEL_TYPE_Regional / Local | CHANNEL_TYPE_Stone | NAME_SELLER_INDUSTRY_Clothing | NAME_SELLER_INDUSTRY_Connectivity | NAME_SELLER_INDUSTRY_Construction | NAME_SELLER_INDUSTRY_Consumer electronics | NAME_SELLER_INDUSTRY_Furniture | NAME_SELLER_INDUSTRY_Industry | NAME_SELLER_INDUSTRY_Jewelry | NAME_SELLER_INDUSTRY_MLM partners | NAME_SELLER_INDUSTRY_Tourism | NAME_SELLER_INDUSTRY_XNA | NAME_YIELD_GROUP_high | NAME_YIELD_GROUP_low_action | NAME_YIELD_GROUP_low_normal | NAME_YIELD_GROUP_middle | PRODUCT_COMBINATION_Card X-Sell | PRODUCT_COMBINATION_Cash | PRODUCT_COMBINATION_Cash Street: high | PRODUCT_COMBINATION_Cash Street: low | PRODUCT_COMBINATION_Cash Street: middle | PRODUCT_COMBINATION_Cash X-Sell: high | PRODUCT_COMBINATION_Cash X-Sell: low | PRODUCT_COMBINATION_Cash X-Sell: middle | PRODUCT_COMBINATION_POS household with interest | PRODUCT_COMBINATION_POS household without interest | PRODUCT_COMBINATION_POS industry with interest | PRODUCT_COMBINATION_POS industry without interest | PRODUCT_COMBINATION_POS mobile with interest | PRODUCT_COMBINATION_POS mobile without interest | PRODUCT_COMBINATION_POS other with interest | PRODUCT_COMBINATION_POS others without interest | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 886912 | -0.565295 | 0.602840 | -0.109029 | 0.237550 | 0.049634 | 1.195677 | -0.040396 | -0.515113 | -0.903322 | 0.827966 | -0.164389 | 2.189600 | -0.46203 | -0.622475 | -0.333640 | -0.621366 | -0.283858 | -0.580375 | -0.072306 | -0.064286 | 5.608317 | 0.774544 | -0.45678 | 1.873421 | 0.006095 | -0.598698 | -0.616825 | 0.008398 | 0.465356 | 0.068673 | 0.059004 | 0.803143 | -0.206051 | -0.232557 | 0.206821 | -0.161489 | -0.250496 | -0.385506 | -0.402045 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1345274 | -0.565295 | 0.602840 | -0.504282 | -0.882596 | -0.382168 | -0.168180 | 1.182491 | -0.504841 | -2.227636 | 0.755375 | -0.164389 | 0.948029 | -0.46203 | 1.002743 | -0.333640 | 1.016886 | -0.283858 | -0.010246 | -0.072306 | -0.064286 | -0.168591 | -0.284108 | -0.45678 | 0.160139 | 0.006095 | -0.598698 | -0.616825 | 0.008398 | -1.047544 | 0.068673 | 0.059004 | 1.089453 | -0.206051 | -0.232557 | 0.206821 | -0.161489 | -0.250496 | -0.385506 | -0.402045 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 710187 | -0.565295 | 0.347182 | -1.097161 | -1.375291 | -1.029872 | -0.098990 | 1.058110 | -0.490013 | 0.844681 | -0.717089 | -0.164389 | 1.568814 | -0.46203 | -0.622475 | -0.333640 | -0.621366 | -0.283858 | -0.112770 | -0.072306 | -0.064286 | -0.168591 | -0.284108 | -0.45678 | -1.124822 | 0.006095 | -0.598698 | -0.616825 | 0.008398 | -0.442384 | 0.068673 | 0.059004 | 1.099543 | -0.206051 | -0.232557 | 0.206821 | -0.161489 | -0.250496 | -0.385506 | -0.402045 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 969891 | -0.565295 | -0.675449 | -0.341531 | -0.175459 | -0.204367 | 0.642704 | 0.976033 | 2.006208 | 1.398933 | 0.255230 | -1.292547 | 0.637636 | -0.46203 | -0.216171 | 1.789523 | -0.211803 | -0.283858 | -1.344297 | -0.072306 | -0.064286 | -0.168591 | -0.284108 | -0.45678 | -1.124822 | -0.516710 | -0.355134 | -0.398309 | -0.563952 | 0.465356 | 0.068673 | 0.059004 | -2.254190 | -0.147522 | -0.388368 | 0.206821 | -0.192560 | -0.273737 | -0.400448 | -0.416799 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 1169924 | -0.565295 | 0.602840 | 2.135401 | 4.713275 | 2.335648 | 0.849497 | 0.541504 | -0.485483 | -0.188488 | -0.569243 | -0.164389 | -0.603936 | -0.46203 | -0.622475 | -0.333640 | -0.621366 | -0.283858 | 2.133986 | -0.072306 | -0.064286 | -0.168591 | -0.284108 | -0.45678 | -1.124822 | -0.712406 | -0.433935 | -0.542631 | -0.647263 | -0.139804 | 0.068673 | 0.059004 | -2.385363 | -0.183651 | -0.855803 | 0.206821 | -0.194333 | -0.277006 | -0.402750 | -0.418678 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
X_test[['CNT_CHILDREN','AMT_INCOME_TOTAL','AMT_CREDIT_x','AMT_ANNUITY_x',
'AMT_GOODS_PRICE_x','REGION_POPULATION_RELATIVE','DAYS_BIRTH',
'DAYS_EMPLOYED','DAYS_REGISTRATION','DAYS_ID_PUBLISH','CNT_FAM_MEMBERS',
'HOUR_APPR_PROCESS_START_x','LIVE_CITY_NOT_WORK_CITY','OBS_30_CNT_SOCIAL_CIRCLE',
'DEF_30_CNT_SOCIAL_CIRCLE','OBS_60_CNT_SOCIAL_CIRCLE','DEF_60_CNT_SOCIAL_CIRCLE',
'DAYS_LAST_PHONE_CHANGE','AMT_REQ_CREDIT_BUREAU_HOUR','AMT_REQ_CREDIT_BUREAU_DAY',
'AMT_REQ_CREDIT_BUREAU_WEEK','AMT_REQ_CREDIT_BUREAU_MON','AMT_REQ_CREDIT_BUREAU_QRT',
'AMT_REQ_CREDIT_BUREAU_YEAR','AMT_ANNUITY_y','AMT_APPLICATION','AMT_CREDIT_y',
'AMT_GOODS_PRICE_y','HOUR_APPR_PROCESS_START_y','FLAG_LAST_APPL_PER_CONTRACT',
'NFLAG_LAST_APPL_IN_DAY','DAYS_DECISION','SELLERPLACE_AREA','CNT_PAYMENT',
'DAYS_FIRST_DRAWING','DAYS_FIRST_DUE','DAYS_LAST_DUE_1ST_VERSION','DAYS_LAST_DUE',
'DAYS_TERMINATION']] = scaler.transform(X_test[['CNT_CHILDREN','AMT_INCOME_TOTAL','AMT_CREDIT_x',
'AMT_ANNUITY_x','AMT_GOODS_PRICE_x','REGION_POPULATION_RELATIVE',
'DAYS_BIRTH','DAYS_EMPLOYED','DAYS_REGISTRATION','DAYS_ID_PUBLISH',
'CNT_FAM_MEMBERS','HOUR_APPR_PROCESS_START_x','LIVE_CITY_NOT_WORK_CITY',
'OBS_30_CNT_SOCIAL_CIRCLE','DEF_30_CNT_SOCIAL_CIRCLE','OBS_60_CNT_SOCIAL_CIRCLE',
'DEF_60_CNT_SOCIAL_CIRCLE','DAYS_LAST_PHONE_CHANGE','AMT_REQ_CREDIT_BUREAU_HOUR',
'AMT_REQ_CREDIT_BUREAU_DAY','AMT_REQ_CREDIT_BUREAU_WEEK','AMT_REQ_CREDIT_BUREAU_MON',
'AMT_REQ_CREDIT_BUREAU_QRT','AMT_REQ_CREDIT_BUREAU_YEAR','AMT_ANNUITY_y','AMT_APPLICATION',
'AMT_CREDIT_y','AMT_GOODS_PRICE_y','HOUR_APPR_PROCESS_START_y','FLAG_LAST_APPL_PER_CONTRACT',
'NFLAG_LAST_APPL_IN_DAY','DAYS_DECISION','SELLERPLACE_AREA','CNT_PAYMENT','DAYS_FIRST_DRAWING',
'DAYS_FIRST_DUE','DAYS_LAST_DUE_1ST_VERSION','DAYS_LAST_DUE','DAYS_TERMINATION']])
X_test.head()
| CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT_x | AMT_ANNUITY_x | AMT_GOODS_PRICE_x | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | CNT_FAM_MEMBERS | HOUR_APPR_PROCESS_START_x | LIVE_CITY_NOT_WORK_CITY | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | AMT_ANNUITY_y | AMT_APPLICATION | AMT_CREDIT_y | AMT_GOODS_PRICE_y | HOUR_APPR_PROCESS_START_y | FLAG_LAST_APPL_PER_CONTRACT | NFLAG_LAST_APPL_IN_DAY | DAYS_DECISION | SELLERPLACE_AREA | CNT_PAYMENT | DAYS_FIRST_DRAWING | DAYS_FIRST_DUE | DAYS_LAST_DUE_1ST_VERSION | DAYS_LAST_DUE | DAYS_TERMINATION | FLAG_OWN_CAR_1 | FLAG_OWN_REALTY_1 | FLAG_EMP_PHONE_1 | FLAG_WORK_PHONE_1 | FLAG_CONT_MOBILE_1 | FLAG_PHONE_1 | FLAG_EMAIL_1 | REGION_RATING_CLIENT_2 | REGION_RATING_CLIENT_3 | REGION_RATING_CLIENT_W_CITY_2 | REGION_RATING_CLIENT_W_CITY_3 | REG_REGION_NOT_LIVE_REGION_1 | REG_REGION_NOT_WORK_REGION_1 | LIVE_REGION_NOT_WORK_REGION_1 | REG_CITY_NOT_LIVE_CITY_1 | REG_CITY_NOT_WORK_CITY_1 | FLAG_DOCUMENT_2_1 | FLAG_DOCUMENT_3_1 | FLAG_DOCUMENT_4_1 | FLAG_DOCUMENT_5_1 | FLAG_DOCUMENT_6_1 | FLAG_DOCUMENT_7_1 | FLAG_DOCUMENT_8_1 | FLAG_DOCUMENT_9_1 | FLAG_DOCUMENT_10_1 | FLAG_DOCUMENT_11_1 | FLAG_DOCUMENT_12_1 | FLAG_DOCUMENT_13_1 | FLAG_DOCUMENT_14_1 | FLAG_DOCUMENT_15_1 | FLAG_DOCUMENT_16_1 | FLAG_DOCUMENT_17_1 | FLAG_DOCUMENT_18_1 | FLAG_DOCUMENT_19_1 | FLAG_DOCUMENT_20_1 | FLAG_DOCUMENT_21_1 | NFLAG_INSURED_ON_APPROVAL_1.0 | NAME_CONTRACT_TYPE_x_Revolving loans | CODE_GENDER_M | CODE_GENDER_XNA | NAME_TYPE_SUITE_x_Family | NAME_TYPE_SUITE_x_Group of people | NAME_TYPE_SUITE_x_Other_A | NAME_TYPE_SUITE_x_Other_B | NAME_TYPE_SUITE_x_Spouse, partner | NAME_TYPE_SUITE_x_Unaccompanied | NAME_INCOME_TYPE_Maternity leave | NAME_INCOME_TYPE_Pensioner | NAME_INCOME_TYPE_State servant | NAME_INCOME_TYPE_Student | NAME_INCOME_TYPE_Unemployed | NAME_INCOME_TYPE_Working | NAME_EDUCATION_TYPE_Higher education | NAME_EDUCATION_TYPE_Incomplete higher | NAME_EDUCATION_TYPE_Lower secondary | NAME_EDUCATION_TYPE_Secondary / secondary special | NAME_FAMILY_STATUS_Married | NAME_FAMILY_STATUS_Separated | NAME_FAMILY_STATUS_Single / not married | NAME_FAMILY_STATUS_Widow | NAME_HOUSING_TYPE_House / apartment | NAME_HOUSING_TYPE_Municipal apartment | NAME_HOUSING_TYPE_Office apartment | NAME_HOUSING_TYPE_Rented apartment | NAME_HOUSING_TYPE_With parents | WEEKDAY_APPR_PROCESS_START_x_MONDAY | WEEKDAY_APPR_PROCESS_START_x_SATURDAY | WEEKDAY_APPR_PROCESS_START_x_SUNDAY | WEEKDAY_APPR_PROCESS_START_x_THURSDAY | WEEKDAY_APPR_PROCESS_START_x_TUESDAY | WEEKDAY_APPR_PROCESS_START_x_WEDNESDAY | ORGANIZATION_TYPE_Agriculture | ORGANIZATION_TYPE_Bank | ORGANIZATION_TYPE_Business Entity Type 1 | ORGANIZATION_TYPE_Business Entity Type 2 | ORGANIZATION_TYPE_Business Entity Type 3 | ORGANIZATION_TYPE_Cleaning | ORGANIZATION_TYPE_Construction | ORGANIZATION_TYPE_Culture | ORGANIZATION_TYPE_Electricity | ORGANIZATION_TYPE_Emergency | ORGANIZATION_TYPE_Government | ORGANIZATION_TYPE_Hotel | ORGANIZATION_TYPE_Housing | ORGANIZATION_TYPE_Industry: type 1 | ORGANIZATION_TYPE_Industry: type 10 | ORGANIZATION_TYPE_Industry: type 11 | ORGANIZATION_TYPE_Industry: type 12 | ORGANIZATION_TYPE_Industry: type 13 | ORGANIZATION_TYPE_Industry: type 2 | ORGANIZATION_TYPE_Industry: type 3 | ORGANIZATION_TYPE_Industry: type 4 | ORGANIZATION_TYPE_Industry: type 5 | ORGANIZATION_TYPE_Industry: type 6 | ORGANIZATION_TYPE_Industry: type 7 | ORGANIZATION_TYPE_Industry: type 8 | ORGANIZATION_TYPE_Industry: type 9 | ORGANIZATION_TYPE_Insurance | ORGANIZATION_TYPE_Kindergarten | ORGANIZATION_TYPE_Legal Services | ORGANIZATION_TYPE_Medicine | ORGANIZATION_TYPE_Military | ORGANIZATION_TYPE_Mobile | ORGANIZATION_TYPE_Other | ORGANIZATION_TYPE_Police | ORGANIZATION_TYPE_Postal | ORGANIZATION_TYPE_Realtor | ORGANIZATION_TYPE_Religion | ORGANIZATION_TYPE_Restaurant | ORGANIZATION_TYPE_School | ORGANIZATION_TYPE_Security | ORGANIZATION_TYPE_Security Ministries | ORGANIZATION_TYPE_Self-employed | ORGANIZATION_TYPE_Services | ORGANIZATION_TYPE_Telecom | ORGANIZATION_TYPE_Trade: type 1 | ORGANIZATION_TYPE_Trade: type 2 | ORGANIZATION_TYPE_Trade: type 3 | ORGANIZATION_TYPE_Trade: type 4 | ORGANIZATION_TYPE_Trade: type 5 | ORGANIZATION_TYPE_Trade: type 6 | ORGANIZATION_TYPE_Trade: type 7 | ORGANIZATION_TYPE_Transport: type 1 | ORGANIZATION_TYPE_Transport: type 2 | ORGANIZATION_TYPE_Transport: type 3 | ORGANIZATION_TYPE_Transport: type 4 | ORGANIZATION_TYPE_University | ORGANIZATION_TYPE_XNA | NAME_CONTRACT_TYPE_y_Consumer loans | NAME_CONTRACT_TYPE_y_Revolving loans | WEEKDAY_APPR_PROCESS_START_y_MONDAY | WEEKDAY_APPR_PROCESS_START_y_SATURDAY | WEEKDAY_APPR_PROCESS_START_y_SUNDAY | WEEKDAY_APPR_PROCESS_START_y_THURSDAY | WEEKDAY_APPR_PROCESS_START_y_TUESDAY | WEEKDAY_APPR_PROCESS_START_y_WEDNESDAY | NAME_CASH_LOAN_PURPOSE_Business development | NAME_CASH_LOAN_PURPOSE_Buying a garage | NAME_CASH_LOAN_PURPOSE_Buying a holiday home / land | NAME_CASH_LOAN_PURPOSE_Buying a home | NAME_CASH_LOAN_PURPOSE_Buying a new car | NAME_CASH_LOAN_PURPOSE_Buying a used car | NAME_CASH_LOAN_PURPOSE_Car repairs | NAME_CASH_LOAN_PURPOSE_Education | NAME_CASH_LOAN_PURPOSE_Everyday expenses | NAME_CASH_LOAN_PURPOSE_Furniture | NAME_CASH_LOAN_PURPOSE_Gasification / water supply | NAME_CASH_LOAN_PURPOSE_Hobby | NAME_CASH_LOAN_PURPOSE_Journey | NAME_CASH_LOAN_PURPOSE_Medicine | NAME_CASH_LOAN_PURPOSE_Money for a third person | NAME_CASH_LOAN_PURPOSE_Other | NAME_CASH_LOAN_PURPOSE_Payments on other loans | NAME_CASH_LOAN_PURPOSE_Purchase of electronic equipment | NAME_CASH_LOAN_PURPOSE_Refusal to name the goal | NAME_CASH_LOAN_PURPOSE_Repairs | NAME_CASH_LOAN_PURPOSE_Urgent needs | NAME_CASH_LOAN_PURPOSE_Wedding / gift / holiday | NAME_CASH_LOAN_PURPOSE_XAP | NAME_CASH_LOAN_PURPOSE_XNA | NAME_CONTRACT_STATUS_Canceled | NAME_CONTRACT_STATUS_Refused | NAME_CONTRACT_STATUS_Unused offer | NAME_PAYMENT_TYPE_Cashless from the account of the employer | NAME_PAYMENT_TYPE_Non-cash from your account | NAME_PAYMENT_TYPE_XNA | CODE_REJECT_REASON_HC | CODE_REJECT_REASON_LIMIT | CODE_REJECT_REASON_SCO | CODE_REJECT_REASON_SCOFR | CODE_REJECT_REASON_SYSTEM | CODE_REJECT_REASON_VERIF | CODE_REJECT_REASON_XAP | CODE_REJECT_REASON_XNA | NAME_TYPE_SUITE_y_Family | NAME_TYPE_SUITE_y_Group of people | NAME_TYPE_SUITE_y_Other_A | NAME_TYPE_SUITE_y_Other_B | NAME_TYPE_SUITE_y_Spouse, partner | NAME_TYPE_SUITE_y_Unaccompanied | NAME_CLIENT_TYPE_Refreshed | NAME_CLIENT_TYPE_Repeater | NAME_CLIENT_TYPE_XNA | NAME_GOODS_CATEGORY_Animals | NAME_GOODS_CATEGORY_Audio/Video | NAME_GOODS_CATEGORY_Auto Accessories | NAME_GOODS_CATEGORY_Clothing and Accessories | NAME_GOODS_CATEGORY_Computers | NAME_GOODS_CATEGORY_Construction Materials | NAME_GOODS_CATEGORY_Consumer Electronics | NAME_GOODS_CATEGORY_Direct Sales | NAME_GOODS_CATEGORY_Education | NAME_GOODS_CATEGORY_Fitness | NAME_GOODS_CATEGORY_Furniture | NAME_GOODS_CATEGORY_Gardening | NAME_GOODS_CATEGORY_Homewares | NAME_GOODS_CATEGORY_Insurance | NAME_GOODS_CATEGORY_Jewelry | NAME_GOODS_CATEGORY_Medical Supplies | NAME_GOODS_CATEGORY_Medicine | NAME_GOODS_CATEGORY_Mobile | NAME_GOODS_CATEGORY_Office Appliances | NAME_GOODS_CATEGORY_Other | NAME_GOODS_CATEGORY_Photo / Cinema Equipment | NAME_GOODS_CATEGORY_Sport and Leisure | NAME_GOODS_CATEGORY_Tourism | NAME_GOODS_CATEGORY_Vehicles | NAME_GOODS_CATEGORY_Weapon | NAME_GOODS_CATEGORY_XNA | NAME_PORTFOLIO_Cars | NAME_PORTFOLIO_Cash | NAME_PORTFOLIO_POS | NAME_PORTFOLIO_XNA | NAME_PRODUCT_TYPE_walk-in | NAME_PRODUCT_TYPE_x-sell | CHANNEL_TYPE_Car dealer | CHANNEL_TYPE_Channel of corporate sales | CHANNEL_TYPE_Contact center | CHANNEL_TYPE_Country-wide | CHANNEL_TYPE_Credit and cash offices | CHANNEL_TYPE_Regional / Local | CHANNEL_TYPE_Stone | NAME_SELLER_INDUSTRY_Clothing | NAME_SELLER_INDUSTRY_Connectivity | NAME_SELLER_INDUSTRY_Construction | NAME_SELLER_INDUSTRY_Consumer electronics | NAME_SELLER_INDUSTRY_Furniture | NAME_SELLER_INDUSTRY_Industry | NAME_SELLER_INDUSTRY_Jewelry | NAME_SELLER_INDUSTRY_MLM partners | NAME_SELLER_INDUSTRY_Tourism | NAME_SELLER_INDUSTRY_XNA | NAME_YIELD_GROUP_high | NAME_YIELD_GROUP_low_action | NAME_YIELD_GROUP_low_normal | NAME_YIELD_GROUP_middle | PRODUCT_COMBINATION_Card X-Sell | PRODUCT_COMBINATION_Cash | PRODUCT_COMBINATION_Cash Street: high | PRODUCT_COMBINATION_Cash Street: low | PRODUCT_COMBINATION_Cash Street: middle | PRODUCT_COMBINATION_Cash X-Sell: high | PRODUCT_COMBINATION_Cash X-Sell: low | PRODUCT_COMBINATION_Cash X-Sell: middle | PRODUCT_COMBINATION_POS household with interest | PRODUCT_COMBINATION_POS household without interest | PRODUCT_COMBINATION_POS industry with interest | PRODUCT_COMBINATION_POS industry without interest | PRODUCT_COMBINATION_POS mobile with interest | PRODUCT_COMBINATION_POS mobile without interest | PRODUCT_COMBINATION_POS other with interest | PRODUCT_COMBINATION_POS others without interest | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 563940 | -0.565295 | 0.347182 | 1.143224 | 1.833548 | 1.065640 | -1.028783 | 0.763137 | 2.006208 | 1.356168 | 0.076750 | -0.164389 | 0.327243 | -0.46203 | 0.190134 | -0.333640 | 0.197760 | -0.283858 | -1.145502 | -0.072306 | -0.064286 | -0.168591 | -0.284108 | -0.456780 | 2.301741 | 0.006095 | -0.598698 | -0.616825 | 0.008398 | -0.139804 | 0.068673 | 0.059004 | 0.910352 | -0.206051 | -0.232557 | 0.206821 | -0.161489 | -0.250496 | -0.385506 | -0.402045 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 186208 | -0.565295 | -0.675449 | -0.293136 | -0.359775 | -0.382168 | 0.187930 | 0.757389 | -0.483930 | 0.290996 | 0.015480 | -0.164389 | -0.914329 | 2.16436 | -0.216171 | 1.789523 | -0.211803 | 2.282384 | 0.924964 | -0.072306 | -0.064286 | -0.168591 | -0.284108 | -0.456780 | -0.696501 | 0.006095 | -0.137463 | -0.192062 | -0.333824 | -1.047544 | 0.068673 | 0.059004 | -1.165709 | 1.154562 | -0.232557 | 0.206821 | -0.161489 | -0.250496 | -0.385506 | -0.402045 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 739774 | -0.565295 | 1.114155 | -0.041720 | -0.618594 | -0.140867 | -0.122260 | 1.261120 | -0.528661 | -0.417609 | 0.634834 | -0.164389 | 0.637636 | -0.46203 | -0.622475 | -0.333640 | -0.621366 | -0.283858 | 0.524874 | -0.072306 | -0.064286 | -0.168591 | -0.284108 | 2.394456 | -0.696501 | -0.177537 | 0.005852 | -0.060079 | -0.182308 | 1.373096 | 0.068673 | 0.059004 | 0.197731 | -0.194489 | -0.076745 | 0.206821 | -0.159357 | -0.249943 | -0.385149 | -0.401966 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 767186 | -0.565295 | -0.931107 | -1.050661 | -0.848583 | -0.979072 | -0.454323 | 1.081101 | 2.006208 | 0.283066 | 0.600204 | -0.164389 | -0.914329 | -0.46203 | -0.216171 | -0.333640 | -0.211803 | -0.283858 | -0.592878 | -0.072306 | -0.064286 | -0.168591 | -0.284108 | 0.968838 | 0.588460 | 0.144102 | -0.176021 | -0.312883 | -0.374589 | 0.162776 | 0.068673 | 0.059004 | 0.365481 | 1.601115 | -0.699992 | 0.206821 | -0.157140 | -0.251210 | -0.384825 | -0.401628 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 502320 | 0.846501 | -0.164134 | -0.230802 | -0.207204 | -0.496469 | -0.122260 | -0.704834 | -0.492301 | -0.608779 | 1.230213 | 0.963769 | -1.845507 | 2.16436 | -0.622475 | -0.333640 | -0.621366 | -0.283858 | -1.344297 | -0.072306 | -0.064286 | -0.168591 | 0.774544 | -0.456780 | 0.160139 | -0.578755 | 0.196804 | 0.230619 | 0.019571 | -1.652704 | 0.068673 | 0.059004 | 0.805666 | -0.206051 | 2.572052 | 0.206821 | -0.151206 | -0.232700 | 2.579944 | 2.474262 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
X_test[['CNT_CHILDREN','AMT_INCOME_TOTAL','AMT_CREDIT_x','AMT_ANNUITY_x',
'AMT_GOODS_PRICE_x','REGION_POPULATION_RELATIVE','DAYS_BIRTH',
'DAYS_EMPLOYED','DAYS_REGISTRATION','DAYS_ID_PUBLISH','CNT_FAM_MEMBERS',
'HOUR_APPR_PROCESS_START_x','LIVE_CITY_NOT_WORK_CITY','OBS_30_CNT_SOCIAL_CIRCLE',
'DEF_30_CNT_SOCIAL_CIRCLE','OBS_60_CNT_SOCIAL_CIRCLE','DEF_60_CNT_SOCIAL_CIRCLE',
'DAYS_LAST_PHONE_CHANGE','AMT_REQ_CREDIT_BUREAU_HOUR','AMT_REQ_CREDIT_BUREAU_DAY',
'AMT_REQ_CREDIT_BUREAU_WEEK','AMT_REQ_CREDIT_BUREAU_MON','AMT_REQ_CREDIT_BUREAU_QRT',
'AMT_REQ_CREDIT_BUREAU_YEAR','AMT_ANNUITY_y','AMT_APPLICATION','AMT_CREDIT_y',
'AMT_GOODS_PRICE_y','HOUR_APPR_PROCESS_START_y','FLAG_LAST_APPL_PER_CONTRACT',
'NFLAG_LAST_APPL_IN_DAY','DAYS_DECISION','SELLERPLACE_AREA','CNT_PAYMENT',
'DAYS_FIRST_DRAWING','DAYS_FIRST_DUE','DAYS_LAST_DUE_1ST_VERSION','DAYS_LAST_DUE',
'DAYS_TERMINATION']] = scaler.transform(X_test[['CNT_CHILDREN','AMT_INCOME_TOTAL','AMT_CREDIT_x',
'AMT_ANNUITY_x','AMT_GOODS_PRICE_x','REGION_POPULATION_RELATIVE',
'DAYS_BIRTH','DAYS_EMPLOYED','DAYS_REGISTRATION','DAYS_ID_PUBLISH',
'CNT_FAM_MEMBERS','HOUR_APPR_PROCESS_START_x','LIVE_CITY_NOT_WORK_CITY',
'OBS_30_CNT_SOCIAL_CIRCLE','DEF_30_CNT_SOCIAL_CIRCLE','OBS_60_CNT_SOCIAL_CIRCLE',
'DEF_60_CNT_SOCIAL_CIRCLE','DAYS_LAST_PHONE_CHANGE','AMT_REQ_CREDIT_BUREAU_HOUR',
'AMT_REQ_CREDIT_BUREAU_DAY','AMT_REQ_CREDIT_BUREAU_WEEK','AMT_REQ_CREDIT_BUREAU_MON',
'AMT_REQ_CREDIT_BUREAU_QRT','AMT_REQ_CREDIT_BUREAU_YEAR','AMT_ANNUITY_y','AMT_APPLICATION',
'AMT_CREDIT_y','AMT_GOODS_PRICE_y','HOUR_APPR_PROCESS_START_y','FLAG_LAST_APPL_PER_CONTRACT',
'NFLAG_LAST_APPL_IN_DAY','DAYS_DECISION','SELLERPLACE_AREA','CNT_PAYMENT','DAYS_FIRST_DRAWING',
'DAYS_FIRST_DUE','DAYS_LAST_DUE_1ST_VERSION','DAYS_LAST_DUE','DAYS_TERMINATION']])
X_test.head()
| CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT_x | AMT_ANNUITY_x | AMT_GOODS_PRICE_x | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | CNT_FAM_MEMBERS | HOUR_APPR_PROCESS_START_x | LIVE_CITY_NOT_WORK_CITY | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | AMT_ANNUITY_y | AMT_APPLICATION | AMT_CREDIT_y | AMT_GOODS_PRICE_y | HOUR_APPR_PROCESS_START_y | FLAG_LAST_APPL_PER_CONTRACT | NFLAG_LAST_APPL_IN_DAY | DAYS_DECISION | SELLERPLACE_AREA | CNT_PAYMENT | DAYS_FIRST_DRAWING | DAYS_FIRST_DUE | DAYS_LAST_DUE_1ST_VERSION | DAYS_LAST_DUE | DAYS_TERMINATION | FLAG_OWN_CAR_1 | FLAG_OWN_REALTY_1 | FLAG_EMP_PHONE_1 | FLAG_WORK_PHONE_1 | FLAG_CONT_MOBILE_1 | FLAG_PHONE_1 | FLAG_EMAIL_1 | REGION_RATING_CLIENT_2 | REGION_RATING_CLIENT_3 | REGION_RATING_CLIENT_W_CITY_2 | REGION_RATING_CLIENT_W_CITY_3 | REG_REGION_NOT_LIVE_REGION_1 | REG_REGION_NOT_WORK_REGION_1 | LIVE_REGION_NOT_WORK_REGION_1 | REG_CITY_NOT_LIVE_CITY_1 | REG_CITY_NOT_WORK_CITY_1 | FLAG_DOCUMENT_2_1 | FLAG_DOCUMENT_3_1 | FLAG_DOCUMENT_4_1 | FLAG_DOCUMENT_5_1 | FLAG_DOCUMENT_6_1 | FLAG_DOCUMENT_7_1 | FLAG_DOCUMENT_8_1 | FLAG_DOCUMENT_9_1 | FLAG_DOCUMENT_10_1 | FLAG_DOCUMENT_11_1 | FLAG_DOCUMENT_12_1 | FLAG_DOCUMENT_13_1 | FLAG_DOCUMENT_14_1 | FLAG_DOCUMENT_15_1 | FLAG_DOCUMENT_16_1 | FLAG_DOCUMENT_17_1 | FLAG_DOCUMENT_18_1 | FLAG_DOCUMENT_19_1 | FLAG_DOCUMENT_20_1 | FLAG_DOCUMENT_21_1 | NFLAG_INSURED_ON_APPROVAL_1.0 | NAME_CONTRACT_TYPE_x_Revolving loans | CODE_GENDER_M | CODE_GENDER_XNA | NAME_TYPE_SUITE_x_Family | NAME_TYPE_SUITE_x_Group of people | NAME_TYPE_SUITE_x_Other_A | NAME_TYPE_SUITE_x_Other_B | NAME_TYPE_SUITE_x_Spouse, partner | NAME_TYPE_SUITE_x_Unaccompanied | NAME_INCOME_TYPE_Maternity leave | NAME_INCOME_TYPE_Pensioner | NAME_INCOME_TYPE_State servant | NAME_INCOME_TYPE_Student | NAME_INCOME_TYPE_Unemployed | NAME_INCOME_TYPE_Working | NAME_EDUCATION_TYPE_Higher education | NAME_EDUCATION_TYPE_Incomplete higher | NAME_EDUCATION_TYPE_Lower secondary | NAME_EDUCATION_TYPE_Secondary / secondary special | NAME_FAMILY_STATUS_Married | NAME_FAMILY_STATUS_Separated | NAME_FAMILY_STATUS_Single / not married | NAME_FAMILY_STATUS_Widow | NAME_HOUSING_TYPE_House / apartment | NAME_HOUSING_TYPE_Municipal apartment | NAME_HOUSING_TYPE_Office apartment | NAME_HOUSING_TYPE_Rented apartment | NAME_HOUSING_TYPE_With parents | WEEKDAY_APPR_PROCESS_START_x_MONDAY | WEEKDAY_APPR_PROCESS_START_x_SATURDAY | WEEKDAY_APPR_PROCESS_START_x_SUNDAY | WEEKDAY_APPR_PROCESS_START_x_THURSDAY | WEEKDAY_APPR_PROCESS_START_x_TUESDAY | WEEKDAY_APPR_PROCESS_START_x_WEDNESDAY | ORGANIZATION_TYPE_Agriculture | ORGANIZATION_TYPE_Bank | ORGANIZATION_TYPE_Business Entity Type 1 | ORGANIZATION_TYPE_Business Entity Type 2 | ORGANIZATION_TYPE_Business Entity Type 3 | ORGANIZATION_TYPE_Cleaning | ORGANIZATION_TYPE_Construction | ORGANIZATION_TYPE_Culture | ORGANIZATION_TYPE_Electricity | ORGANIZATION_TYPE_Emergency | ORGANIZATION_TYPE_Government | ORGANIZATION_TYPE_Hotel | ORGANIZATION_TYPE_Housing | ORGANIZATION_TYPE_Industry: type 1 | ORGANIZATION_TYPE_Industry: type 10 | ORGANIZATION_TYPE_Industry: type 11 | ORGANIZATION_TYPE_Industry: type 12 | ORGANIZATION_TYPE_Industry: type 13 | ORGANIZATION_TYPE_Industry: type 2 | ORGANIZATION_TYPE_Industry: type 3 | ORGANIZATION_TYPE_Industry: type 4 | ORGANIZATION_TYPE_Industry: type 5 | ORGANIZATION_TYPE_Industry: type 6 | ORGANIZATION_TYPE_Industry: type 7 | ORGANIZATION_TYPE_Industry: type 8 | ORGANIZATION_TYPE_Industry: type 9 | ORGANIZATION_TYPE_Insurance | ORGANIZATION_TYPE_Kindergarten | ORGANIZATION_TYPE_Legal Services | ORGANIZATION_TYPE_Medicine | ORGANIZATION_TYPE_Military | ORGANIZATION_TYPE_Mobile | ORGANIZATION_TYPE_Other | ORGANIZATION_TYPE_Police | ORGANIZATION_TYPE_Postal | ORGANIZATION_TYPE_Realtor | ORGANIZATION_TYPE_Religion | ORGANIZATION_TYPE_Restaurant | ORGANIZATION_TYPE_School | ORGANIZATION_TYPE_Security | ORGANIZATION_TYPE_Security Ministries | ORGANIZATION_TYPE_Self-employed | ORGANIZATION_TYPE_Services | ORGANIZATION_TYPE_Telecom | ORGANIZATION_TYPE_Trade: type 1 | ORGANIZATION_TYPE_Trade: type 2 | ORGANIZATION_TYPE_Trade: type 3 | ORGANIZATION_TYPE_Trade: type 4 | ORGANIZATION_TYPE_Trade: type 5 | ORGANIZATION_TYPE_Trade: type 6 | ORGANIZATION_TYPE_Trade: type 7 | ORGANIZATION_TYPE_Transport: type 1 | ORGANIZATION_TYPE_Transport: type 2 | ORGANIZATION_TYPE_Transport: type 3 | ORGANIZATION_TYPE_Transport: type 4 | ORGANIZATION_TYPE_University | ORGANIZATION_TYPE_XNA | NAME_CONTRACT_TYPE_y_Consumer loans | NAME_CONTRACT_TYPE_y_Revolving loans | WEEKDAY_APPR_PROCESS_START_y_MONDAY | WEEKDAY_APPR_PROCESS_START_y_SATURDAY | WEEKDAY_APPR_PROCESS_START_y_SUNDAY | WEEKDAY_APPR_PROCESS_START_y_THURSDAY | WEEKDAY_APPR_PROCESS_START_y_TUESDAY | WEEKDAY_APPR_PROCESS_START_y_WEDNESDAY | NAME_CASH_LOAN_PURPOSE_Business development | NAME_CASH_LOAN_PURPOSE_Buying a garage | NAME_CASH_LOAN_PURPOSE_Buying a holiday home / land | NAME_CASH_LOAN_PURPOSE_Buying a home | NAME_CASH_LOAN_PURPOSE_Buying a new car | NAME_CASH_LOAN_PURPOSE_Buying a used car | NAME_CASH_LOAN_PURPOSE_Car repairs | NAME_CASH_LOAN_PURPOSE_Education | NAME_CASH_LOAN_PURPOSE_Everyday expenses | NAME_CASH_LOAN_PURPOSE_Furniture | NAME_CASH_LOAN_PURPOSE_Gasification / water supply | NAME_CASH_LOAN_PURPOSE_Hobby | NAME_CASH_LOAN_PURPOSE_Journey | NAME_CASH_LOAN_PURPOSE_Medicine | NAME_CASH_LOAN_PURPOSE_Money for a third person | NAME_CASH_LOAN_PURPOSE_Other | NAME_CASH_LOAN_PURPOSE_Payments on other loans | NAME_CASH_LOAN_PURPOSE_Purchase of electronic equipment | NAME_CASH_LOAN_PURPOSE_Refusal to name the goal | NAME_CASH_LOAN_PURPOSE_Repairs | NAME_CASH_LOAN_PURPOSE_Urgent needs | NAME_CASH_LOAN_PURPOSE_Wedding / gift / holiday | NAME_CASH_LOAN_PURPOSE_XAP | NAME_CASH_LOAN_PURPOSE_XNA | NAME_CONTRACT_STATUS_Canceled | NAME_CONTRACT_STATUS_Refused | NAME_CONTRACT_STATUS_Unused offer | NAME_PAYMENT_TYPE_Cashless from the account of the employer | NAME_PAYMENT_TYPE_Non-cash from your account | NAME_PAYMENT_TYPE_XNA | CODE_REJECT_REASON_HC | CODE_REJECT_REASON_LIMIT | CODE_REJECT_REASON_SCO | CODE_REJECT_REASON_SCOFR | CODE_REJECT_REASON_SYSTEM | CODE_REJECT_REASON_VERIF | CODE_REJECT_REASON_XAP | CODE_REJECT_REASON_XNA | NAME_TYPE_SUITE_y_Family | NAME_TYPE_SUITE_y_Group of people | NAME_TYPE_SUITE_y_Other_A | NAME_TYPE_SUITE_y_Other_B | NAME_TYPE_SUITE_y_Spouse, partner | NAME_TYPE_SUITE_y_Unaccompanied | NAME_CLIENT_TYPE_Refreshed | NAME_CLIENT_TYPE_Repeater | NAME_CLIENT_TYPE_XNA | NAME_GOODS_CATEGORY_Animals | NAME_GOODS_CATEGORY_Audio/Video | NAME_GOODS_CATEGORY_Auto Accessories | NAME_GOODS_CATEGORY_Clothing and Accessories | NAME_GOODS_CATEGORY_Computers | NAME_GOODS_CATEGORY_Construction Materials | NAME_GOODS_CATEGORY_Consumer Electronics | NAME_GOODS_CATEGORY_Direct Sales | NAME_GOODS_CATEGORY_Education | NAME_GOODS_CATEGORY_Fitness | NAME_GOODS_CATEGORY_Furniture | NAME_GOODS_CATEGORY_Gardening | NAME_GOODS_CATEGORY_Homewares | NAME_GOODS_CATEGORY_Insurance | NAME_GOODS_CATEGORY_Jewelry | NAME_GOODS_CATEGORY_Medical Supplies | NAME_GOODS_CATEGORY_Medicine | NAME_GOODS_CATEGORY_Mobile | NAME_GOODS_CATEGORY_Office Appliances | NAME_GOODS_CATEGORY_Other | NAME_GOODS_CATEGORY_Photo / Cinema Equipment | NAME_GOODS_CATEGORY_Sport and Leisure | NAME_GOODS_CATEGORY_Tourism | NAME_GOODS_CATEGORY_Vehicles | NAME_GOODS_CATEGORY_Weapon | NAME_GOODS_CATEGORY_XNA | NAME_PORTFOLIO_Cars | NAME_PORTFOLIO_Cash | NAME_PORTFOLIO_POS | NAME_PORTFOLIO_XNA | NAME_PRODUCT_TYPE_walk-in | NAME_PRODUCT_TYPE_x-sell | CHANNEL_TYPE_Car dealer | CHANNEL_TYPE_Channel of corporate sales | CHANNEL_TYPE_Contact center | CHANNEL_TYPE_Country-wide | CHANNEL_TYPE_Credit and cash offices | CHANNEL_TYPE_Regional / Local | CHANNEL_TYPE_Stone | NAME_SELLER_INDUSTRY_Clothing | NAME_SELLER_INDUSTRY_Connectivity | NAME_SELLER_INDUSTRY_Construction | NAME_SELLER_INDUSTRY_Consumer electronics | NAME_SELLER_INDUSTRY_Furniture | NAME_SELLER_INDUSTRY_Industry | NAME_SELLER_INDUSTRY_Jewelry | NAME_SELLER_INDUSTRY_MLM partners | NAME_SELLER_INDUSTRY_Tourism | NAME_SELLER_INDUSTRY_XNA | NAME_YIELD_GROUP_high | NAME_YIELD_GROUP_low_action | NAME_YIELD_GROUP_low_normal | NAME_YIELD_GROUP_middle | PRODUCT_COMBINATION_Card X-Sell | PRODUCT_COMBINATION_Cash | PRODUCT_COMBINATION_Cash Street: high | PRODUCT_COMBINATION_Cash Street: low | PRODUCT_COMBINATION_Cash Street: middle | PRODUCT_COMBINATION_Cash X-Sell: high | PRODUCT_COMBINATION_Cash X-Sell: low | PRODUCT_COMBINATION_Cash X-Sell: middle | PRODUCT_COMBINATION_POS household with interest | PRODUCT_COMBINATION_POS household without interest | PRODUCT_COMBINATION_POS industry with interest | PRODUCT_COMBINATION_POS industry without interest | PRODUCT_COMBINATION_POS mobile with interest | PRODUCT_COMBINATION_POS mobile without interest | PRODUCT_COMBINATION_POS other with interest | PRODUCT_COMBINATION_POS others without interest | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 563940 | -1.363375 | -1.953734 | -1.504036 | -1.942035 | -1.474372 | -81.379969 | -3.750036 | -0.481628 | 1.405831 | -2.038992 | -2.606161 | -3.606291 | -1.675502 | -0.545223 | -1.042012 | -0.540370 | -1.012305 | -1.345729 | -0.955689 | -0.984741 | -1.142528 | -0.584879 | -1.107973 | -0.138939 | -1.228691 | -0.598700 | -0.616827 | -0.821454 | -3.813066 | -13.556997 | -15.944536 | 1.124656 | -0.205477 | -1.185544 | -4.821163 | -0.147423 | -0.246378 | -0.381180 | -0.398163 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 186208 | -1.363375 | -1.953745 | -1.504039 | -1.942193 | -1.474376 | 12.996496 | -3.750038 | -0.481645 | 1.405530 | -2.039033 | -2.606161 | -3.991666 | 5.222424 | -0.710306 | 3.465808 | -0.708112 | 5.573290 | -1.343140 | -0.955689 | -0.984741 | -1.142528 | -0.584879 | -1.107973 | -1.423148 | -1.228691 | -0.598698 | -0.616825 | -0.821455 | -4.087730 | -13.556997 | -15.944536 | 1.122037 | -0.204494 | -1.185544 | -4.821163 | -0.147423 | -0.246378 | -0.381180 | -0.398163 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 739774 | -1.363375 | -1.953725 | -1.504039 | -1.942211 | -1.474376 | -11.063907 | -3.749922 | -0.481645 | 1.405329 | -2.038621 | -2.606161 | -3.509947 | -1.675502 | -0.875390 | -1.042012 | -0.875854 | -1.012305 | -1.343640 | -0.955689 | -0.984741 | -1.142528 | -0.584879 | 2.956800 | -1.423148 | -1.228705 | -0.598698 | -0.616825 | -0.821455 | -3.355293 | -13.556997 | -15.944536 | 1.123757 | -0.205469 | -1.173405 | -4.821163 | -0.147423 | -0.246378 | -0.381180 | -0.398163 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 767186 | -1.363375 | -1.953748 | -1.504041 | -1.942228 | -1.474378 | -36.820993 | -3.749963 | -0.481628 | 1.405527 | -2.038644 | -2.606161 | -3.991666 | -1.675502 | -0.710306 | -1.042012 | -0.708112 | -1.012305 | -1.345038 | -0.955689 | -0.984741 | -1.142528 | -0.584879 | 0.924413 | -0.872773 | -1.228680 | -0.598699 | -0.616826 | -0.821455 | -3.721512 | -13.556997 | -15.944536 | 1.123968 | -0.204171 | -1.221960 | -4.821163 | -0.147423 | -0.246378 | -0.381180 | -0.398163 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 502320 | 0.629792 | -1.953739 | -1.504039 | -1.942182 | -1.474377 | -11.063907 | -3.750374 | -0.481645 | 1.405275 | -2.038224 | -1.333421 | -4.280697 | 5.222424 | -0.875390 | -1.042012 | -0.875854 | -1.012305 | -1.345977 | -0.955689 | -0.984741 | -1.142528 | 0.535865 | -1.107973 | -1.056231 | -1.228736 | -0.598697 | -0.616824 | -0.821454 | -4.270840 | -13.556997 | -15.944536 | 1.124524 | -0.205477 | -0.967049 | -4.821163 | -0.147423 | -0.246378 | -0.381156 | -0.398141 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Target = round((sum(mergeddf['TARGET'])/len(mergeddf['TARGET'].index))*100,2)
print("We have almost {} % Converted rate after successful data manipulation".format(Target))
We have almost 8.66 % Converted rate after successful data manipulation
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
model = KNeighborsClassifier()
model.fit(X_train,y_train)
KNeighborsClassifier()
predict_train = model.predict(X_train)
predict_train
array([0, 0, 0, ..., 0, 0, 0], dtype=int64)
trainaccuracy = accuracy_score(y_train,predict_train)
print('accuracy_score on train dataset : ', trainaccuracy)
accuracy_score on train dataset : 0.9061224489795918
X_train.shape
(4900, 291)
X_test.shape
(2100, 291)
model.fit(X_train,y_train)
KNeighborsClassifier()
predict_train = model.predict(X_train)
predict_train
array([0, 0, 0, ..., 0, 0, 0], dtype=int64)
trainaccuracy = accuracy_score(y_train,predict_train)
print('accuracy_score on train dataset : ', trainaccuracy)
accuracy_score on train dataset : 0.9061224489795918
from sklearn import metrics
# Confusion matrix
confusion = metrics.confusion_matrix(y_train, predict_train )
print(confusion)
[[4411 20] [ 440 29]]
TP = confusion[1,1] # true positive
TN = confusion[0,0] # true negatives
FP = confusion[0,1] # false positives
FN = confusion[1,0] # false negatives
# Let's see the sensitivity of our model
trainsensitivity= TP / float(TP+FN)
trainsensitivity
0.06183368869936034
# Let us calculate specificity
trainspecificity= TN / float(TN+FP)
trainspecificity
0.9954863461972466
# Calculate false postive rate - predicting Defaulted when customer does not have Defaulted
print(FP/ float(TN+FP))
0.0045136538027533285
# Positive predictive value
print (TP / float(TP+FP))
0.5918367346938775
print(TN / float(TN+ FN))
0.909297052154195
def draw_roc( actual, probs ):
fpr, tpr, thresholds = metrics.roc_curve( actual, probs,
drop_intermediate = False )
auc_score = metrics.roc_auc_score( actual, probs )
plt.figure(figsize=(5, 5))
plt.plot( fpr, tpr, label='ROC curve (area = %0.2f)' % auc_score )
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate or [1 - True Negative Rate]')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.show()
return None
draw_roc(y_train,predict_train)
from sklearn.metrics import precision_score, recall_score
precision_score(y_train,predict_train)
0.5918367346938775
recall_score(y_train,predict_train)
0.06183368869936034
# predict the target on the test dataset
predict_test = model.predict(X_test)
print('Target on test data\n\n',predict_test)
Target on test data [0 0 0 ... 0 0 0]
confusion2 = metrics.confusion_matrix(y_test, predict_test )
print(confusion2)
[[1928 0] [ 172 0]]
TP = confusion2[1,1] # true positive
TN = confusion2[0,0] # true negatives
FP = confusion2[0,1] # false positives
FN = confusion2[1,0] # false negatives
# Let's check the overall accuracy.
testaccuracy= accuracy_score(y_test,predict_test)
testaccuracy
0.9180952380952381
# Let's see the sensitivity of our lmodel
testsensitivity=TP / float(TP+FN)
testsensitivity
0.0
# Let us calculate specificity
testspecificity= TN / float(TN+FP)
testspecificity
1.0
# Let us compare the values obtained for Train & Test:
print("Train Data Accuracy :{} %".format(round((trainaccuracy*100),2)))
print("Train Data Sensitivity :{} %".format(round((trainsensitivity*100),2)))
print("Train Data Specificity :{} %".format(round((trainspecificity*100),2)))
print("Test Data Accuracy :{} %".format(round((testaccuracy*100),2)))
print("Test Data Sensitivity :{} %".format(round((testsensitivity*100),2)))
print("Test Data Specificity :{} %".format(round((testspecificity*100),2)))
Train Data Accuracy :90.61 % Train Data Sensitivity :6.18 % Train Data Specificity :99.55 % Test Data Accuracy :91.81 % Test Data Sensitivity :0.0 % Test Data Specificity :100.0 %